Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process
Type: figure
Figure

Image /page/11/Figure/2 description: A flowchart titled 'Calculation process' illustrates a system for predicting geological and forest fire risks. The process begins with two inputs: 'Weather forecast data for the next 7 days (updated at 8:00 and 20:00 daily)' and 'Weather monitoring data (lag about 10-20 minutes, updated every 5 minutes)'. Both are fed as 'input' into a process called 'Instance layer dynamic update'. The output of this stage is then used as 'input' for the central calculation step, 'The triplet disaster prediction model starts the calculation'. This model also utilizes 'Spatio-temporal knowledge graph data'. The model produces two 'output' streams: 'Prediction results of geological landslide risk in the next 7 days' and 'Forecast results of forest fire risk in the next 7 days'. The flowchart also includes two dashed lines indicating dependencies. The top line states, 'Forecast days depend on the availability of weather forecast data', connecting the forecast data to the final predictions. The bottom line states, 'Actual real-time performance depends on the lag of meteorological monitoring data', connecting the monitoring data to the final predictions.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process
Type: figure
Figure: Figure 13. Workflow of disaster dynamic prediction.

Figure 13. Workflow of disaster dynamic prediction.

data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process
Type: text

This paper designs a chain-type automatic reasoning method based on the reasoning criterion. The method divides the calculation logic of the geological landslide prediction model into a series of antecedents and consequences. The antecedents are execution conditions that contain a series of geographic entities. When there exists an intersection of the geographic entities and the conditions set in the antecedent are met, the subsequent associated with the antecedent, i.e., the action object, will be triggered.

It establishes the automatic flow logic between independent criteria such as time probability, space probability, intensity probability, vulnerability of hazard-affected body, and value amount by defining for RuleObject. In this way, it can analyze the relations between time information and space information, which is not feasible using the production reasoning with SWRL.


Document: 1. Introduction
Section: 3. Results
Type: text

This section demonstrates experiments and cases studies of the forest fire and geological landslide risk based on DPKG. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster predictions.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: text

In this case study, Yanyuan County, in the Liangshan Yi Autonomous Prefecture of China's Sichuan Province, was selected to carry out a dynamic prediction experiment of forest fire disasters. Yanyuan County is located between  $100^{\circ}42'09''-102^{\circ}03'44''$  east longitude and  $27^{\circ}06'31-28^{\circ}16'31''$  north latitude, with a total area of 8398.6 square kilometers. There are rich vegetation and many miscellaneous irrigation and thatch grass in the area. The forest fire disasters are easily caused by the inducing factors such as low rainfall. Therefore, Yanyuan County strongly needs to forecast the situation in advance.

In this case, multi-source heterogeneous data related to forest fire risk prediction are collected, such as vegetation data, terrain data, meteorological data, and land cover data. In order to fully represent the spatio-temporal features of the above data in the DPKG knowledge graph, this paper uses Protégé to construct a time ontology and a space ontology. The concepts related to forest fire prediction form the conceptual layer of DPKG. For the above-mentioned multi-source heterogeneous data, we built a diversified knowledge extraction method for transferring the data into triples according to the semantics of the conceptual layer.

The triples form an instance layer of the DPKG of disaster prediction. An example of the extraction process of the slope in the terrain data is shown in Figure 14.

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Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
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Figure

Image /page/12/Figure/1 description: A diagram illustrates a data processing workflow for geographic information. The flow starts with a TIFF file, a raster image of a geographical area. An arrow labeled 'Raster to Vector' points to a Shapefile, which is a colored vector map of the same area. From the Shapefile, an arrow labeled 'Vector to GeoJSON' points to a code snippet of a GeoJSON file. The GeoJSON code shows a 'FeatureCollection' with properties for a 'Slope'. An arrow labeled 'GeoJSON to Triple' points from the GeoJSON to a knowledge graph representation. This graph has a central node 'SlopeEntity' connected to four other nodes via labeled edges: 'hasSlope' points to '21', 'hasTileCode' points to 'z20\_x322\_y786', 'hasTime' points to '2021-10-27 01:17', and 'hasCenterLonLat' points to '102.98438°E, 26.502315°N'.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: figure
Figure: Figure 14. Knowledge extraction process using slope as an example.

Figure 14. Knowledge extraction process using slope as an example.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: text

Forest fire risk prediction needs the theoretical support because the above spatiotemporal data is only a prerequisite for forest fire risk prediction. In this case, the AHP (Analytic Hierarchy Process) is used to evaluate forest fire risk indicators. This paper builds an indicator system for four types of forest fire risk factors: meteorology, terrain, vegetation, and man-made. The weights are shown in Figure 15.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
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Figure

Image /page/12/Figure/4 description: A flowchart diagram illustrating the 'Comprehensive Index of Forest Fire Drivers'. This main index branches into four primary factors, each with a numerical weight. The 'Vegetation factor' has a weight of 0.24 and is subdivided into 'Vegetation Types' (0.18) and 'Canopy' (0.06). The 'Meteorological factor' has the highest weight of 0.40 and is broken down into four sub-factors: 'Wind speed' (0.10), 'Precipitation' (0.10), 'Temperature' (0.10), and 'Humidity' (0.10). The 'Terrain factor' has a weight of 0.21 and includes 'Slope' (0.10), 'Aspect' (0.06), and 'Height' (0.05). Finally, the 'Human factor' has a weight of 0.15 and is represented by a single sub-factor, 'Festival' (0.15).


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
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Figure: Figure 15. Forest fire risk factor indicator system.

Figure 15. Forest fire risk factor indicator system.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: text

The comprehensive indicator of forest fire driving factors is defined as Formula (4):

$$CIFFD = \sum v_i \times w_i \ i \in [1, 10], \tag{4}$$

where *CIFFD* represents a composite index of forest fire drivers;  $v_i$  indicates the level corresponding to the original value of the factor, and  $w_i$  indicates the weight corresponding to the factor. The larger the CIFFD, the greater the risk of forest fires.

In this case, 10 factors in four categories of terrain, vegetation, meteorology, and human factors in the experimental area are used as the query input for the DPKG instance layer. The definition in Formula (4) is transformed into spatio-temporal semantic reasoning rules. The reference output is a comprehensive index of forest fire driving factors.

Define spatio-temporal semantic inference rule 1: If the update of meteorological data is detected, it divides the spatial range of the meteorological data update into several rectangular areas. The rectangular areas automatically triggers the query of 10 factors of

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terrain, vegetation, meteorology, and human factors in the rectangular areas for obtaining the original value of the factor.

Define spatio-temporal semantic inference rule 2: It obtains the corresponding level value based on the original value of the factor. Based on Formula (4), the comprehensive index of forest fire driving factors is obtained.

Define spatio-temporal semantic inference rule 3: Based on the natural discontinuity method, the calculated comprehensive index of forest fire driving factors is divided into 7 intervals for representing different degrees from low to high. The forest fire risk warning information of the experimental area is obtained, as shown in Figure 16.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
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Figure

Image /page/13/Figure/4 description: A flowchart diagram titled "Semantic inference of forest fire disaster". The diagram is divided into three vertical sections by dashed lines, labeled "Rule one", "Rule two", and "Rule three".


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: text

In "Rule one", the process starts with "Meteorological data update", which leads to "Divide rectangular area". An arrow points from this to a box labeled "Query regional forest fire-related factors".

An arrow from "Rule one" points to "Rule two". In this section, there are two icons: a square with a diagonal line and a circle inside, and a stacked triangle. Below them, the text reads "Gets the corresponding level value". This leads to a box labeled "Forest fire driver composite index".

An arrow from "Rule two" points to "Rule three". In this final section, there is a bar chart with bars of increasing height, labeled "Forest fire driving factor composite index divided into intervals". The process concludes in a box labeled "Get rank".


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: figure
Figure: Figure 16. Forest fire risk factor index system.

Figure 16. Forest fire risk factor index system.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: text

The antecedent rules in the above spatio-temporal semantic inference rules can trigger the subsequent rules when satisfying the conditions. Therefore, the dynamic changes of meteorological data can automatically trigger the forest fire risk calculation. It realizes the automatic completion of the chain reasoning of the whole process of DPKG without manual effort.

In this case, the actual meteorological record data of Yanyuan County Meteorological Observatory on 3 January 2022 is used as the query input of the instance layer of the DPKG. The output is calculated based on the reasoning of the DPKG.

For the forest fire risk index of the whole county of Yanyuan, the larger the index value, the higher the forest fire risk. The experimental results are shown in Figure 17.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: figure
Figure

Image /page/13/Figure/9 description: A map displaying the Forest Fire Risk Index for an irregularly shaped geographical area. The map uses a color scale to represent different levels of risk. A legend on the left side details the index values corresponding to each color: dark green for 0.85-2.90, medium green for 2.91-3.60, light green for 3.61-4.00, yellow for 4.01-4.10, light orange for 4.11-4.20, orange for 4.21-4.60, and red for 4.61-5.29. Scattered across the map are several purple dots, which the legend identifies as 'January 3, 2022 06:35 fire point'. In the bottom right corner, there is a scale bar marked in miles, with increments shown at 0, 3, 6, 12, 18, and 24 miles.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: figure
Figure: Figure 17. Map of landslide hazards in Yanyuan County based on multi-Graded Cascade Random Forest.

**Figure 17.** Map of landslide hazards in Yanyuan County based on multi-Graded Cascade Random Forest.


Document: 1. Introduction
Section: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction
Type: text

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NASA's Fire Information for Resource Management System shows the actual fire point in Yanyuan County at 6:35 on 3 January 2022, as shown in Figure 17. The results show that the proposed method successfully hits the fire point of the forest fire species within the time and space range of the predicted forest fire risk index greater than 4.21.

The case uses the traditional spatial analysis tool ArcGIS as a baseline method to calculate the forest fire risk in Yanyuan County for the comparative experiments. It is necessary to load and query Yanyuan County data from 14 types of raster datasets. Steps such as uniform coordinate system, uniform pixel size, grid cropping, and stitching are performed on each raster data, and then the prediction calculation can be performed. There are many steps in the baseline operation, and the whole process takes more than 190 min. When using the grid calculator, the performance is low because it needs complex numerical computations in multiple steps.

In addition, the boundaries of various data are not completely coincident, which will lead to missing or abnormal results of the boundary area analysis, and wrong predictions. Although it is possible to forcibly align the pixel positions by means of translation, it will cause errors by an operation that lacks a realistic basis, and changes the spatial distribution of the original data. In addition, it has a low prediction efficiency when using the traditional spatial analysis tools because they cannot process other target regions in parallel.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: text

In another case study, Xiji County in Guyuan City of China's Ningxia Hui Autonomous Region is selected to carry out an experiment regarding the dynamic prediction of geological landslide hazards. Xiji County locates between  $105^{\circ}20'-106^{\circ}04'$  east longitude and  $35^{\circ}35'-36^{\circ}14'$  north latitude, which has a total area of 3130 square kilometers. In this area, the loess landslide disaster caused by the Haiyuan earthquake is particularly serious because of the special geological environmental conditions. It has formed 765 geological landslides [22]. It is very dangerous that new geological landslide disasters are easily generated by the inducing factors, e.g., rainfall. Therefore, it is urgent to take an effective method to predict disaster in advance.

In this case, the basic geological data, basic geographic data, hydrological data, human activities, and land is collected for the prediction and calculation of geological landslide risk. In order to fully represent the spatio-temporal features of the above data in the knowledge graph, this paper uses Protégé [23] to construct a time ontology and a space ontology, and concepts related to geological landslide prediction form the conceptual layer of spatial-temporal knowledge graph for disaster prediction. For the above-mentioned multi-source heterogeneous data, a diversified knowledge extraction method is adopted to convert the data into triples according to the semantics of the conceptual layer. It forms the instance layer of the DPKG for disaster prediction using the triples.

The above spatio-temporal data is only a prerequisite for geological landslide risk prediction. It needs the theoretical support of the geological landslide risk prediction. The landslide risk assessment is defined as Formula (2). Geological landslide risk prediction involves time, spatial, and intensity probability. In this case, there are 13 factors belonging to four categories of topography, geology, lithology, meteorology and hydrology, land cover, and human activities in the experimental area. The information of the factors is queried from the knowledge graph. It takes the DEM factors in the topographic data as the query input of the instance layer of the knowledge graph. The linear fitting model between historical landslides and DEM data is transformed into spatio-temporal semantic reasoning rules, as shown in Figure 18. In this way, the spatial probability of landslide disasters in the experimental area can be calculated.

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Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure

Image /page/15/Figure/1 description: A flowchart illustrating a model for calculating Geological Landslide Risk Probability. At the top, a computer monitor icon is labeled "Geological Landslide Risk Probability," which points to a formula: H = P(T) × P(S) × P(I). The flowchart has two main input branches. The first branch combines three inputs: "The local precipitation data for the past 10 days is an empirical function of the independent variable," "Deep Random Forest Model," and "Linear fit model between historical landslides and DEM data." These are processed through a step called "Knowledge Representation" to generate "Spatiotemporal Semantic Reasoning Rules." The second branch combines three other inputs: "Precipitation rate," "Topography, Geology and lithology, Meteorology and hydrology, Land cover and human activities," and "DEM." These are processed through a step called "Instantiate" to create a "Disaster prediction spatiotemporal knowledge graph instance layer." This instance layer provides "Data Support" to the "Spatiotemporal Semantic Reasoning Rules." Finally, the "Spatiotemporal Semantic Reasoning Rules" are used to derive the three probabilities P(T), P(S), and P(I) for the final risk calculation.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: figure
Figure: Figure 18. Geological landslide risk prediction calculation formula.

Figure 18. Geological landslide risk prediction calculation formula.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: text

The time probability is a dynamic element. In this case, the time probability is defined as the empirical function with the independent variable of the local precipitation data in the past 10 days, as shown in Formula (3). It transforms Formula (3) into spatio-temporal semantic inference rules, and the time probability of landslide disaster occurrence in the experimental area.

Define spatio-temporal semantic inference rule 1: If the time probability satisfies specific conditions, it is defined as a dangerous time probability.

Define spatio-temporal semantic reasoning rule 2: For a new dangerous time probability, the calculation of the spatial probability and the intensity probability will be automatically triggered. The spatial distribution of the geological landslide risk value in the test area will be calculated according to Formula (1).

Define spatio-temporal semantic inference rule 3: Based on the natural discontinuity method, the geological landslide risk probability value calculated by the previous rule is divided into 10 intervals in ascending order, representing different degrees, every two intervals are combined into a grade for a total of five grades, as shown in Figure 19.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: figure
Figure

Image /page/15/Figure/7 description: A flowchart diagram titled "Semantic reasoning of landslide disaster" is divided into three sections labeled "Rule one," "Rule two," and "Rule three." In "Rule one," two inputs, "Day time probability" and "Nearest time interval time probability," feed into a box labeled "Time probability of dangerous geological landslide." An arrow from this box leads to "Rule two." In "Rule two," the process is described as "Geographic grid computing," represented by an icon of a map with a grid overlay. This process results in the "Spatial distribution of geological landslide risk values." An arrow from this result leads to "Rule three." In "Rule three," the "Natural discontinuity method" is applied to the risk values. The outcome is visualized as five vertical bars of increasing height, representing different risk levels labeled below as "VL, L, M, H, VH," which likely stand for Very Low, Low, Medium, High, and Very High.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: figure
Figure: Figure 19. Space-time semantic reasoning logic with criteria (VL: very low, L: low, M: moderate, H: high, VH: very high).

**Figure 19.** Space-time semantic reasoning logic with criteria (VL: very low, L: low, M: moderate, H: high, VH: very high).


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: text

The antecedent rules in the above spatio-temporal semantic inference rules can trigger the subsequent rules when the conditions are satisfied. Therefore, the dynamic change of precipitation data can automatically trigger the geological landslide risk calculation for

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realizing the automatic completion of the whole process of DPKG chain reasoning without manual effort.

In this case, the actual precipitation record data of Xiji County Meteorological Observatory on 18 September 2018 is used as the query input of the instance layer of the DPKG. Based on the reasoning of spatial-temporal knowledge graph, the geological landslide time probability was daily calculated for the whole county of Xiji from 8 to 28 September 2018. From 18 to 19 September 2018, the geological landslide time probability in the county met the above geological landslide risk time probability conditions for the two consecutive days. The experimental results are shown in Figure 20. Due to the setting of reasoning rule 2, the calculation of geological landslide risk will be triggered automatically. The calculation results of geological landslide risk probability on 18 September 2018 are shown in Figure 21.

The graph of the time probability of landslide


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure

Image /page/16/Figure/4 description: A bar chart titled 'The graph of the time probability of landslide'. The vertical axis is labeled '%0' and ranges from 0 to 20 in increments of 2. The horizontal axis displays dates from 9/8 to 9/28. The chart consists of blue vertical bars for each day, showing the time probability. There are notable peaks in probability on 9/15, with a value of approximately 17.8, and on 9/19, with a value of approximately 18.5. Another smaller peak occurs on 9/18 with a value of about 16. For most other days, the probability fluctuates between approximately 6 and 10. A dashed blue line is overlaid on the chart, starting at a value of about 14, dipping to around 13, and then leveling off at approximately 12.5 for the remainder of the dates.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: text

Bar: The graph of the time probability of landslide in Xiji County from September 8 to 28, 2018

Broken line: The graph of dangerous time probability of landslide in Xiji Countyfrom September 8 to 28,2018


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: figure
Figure: Figure 20. The graph of the time probability of landslide. The bar is the graph of the time probability of landslide in Xiji County from 8–28 September 2018. The broken line is the graph of dangerous time probability of landslide in Xiji County from 8–28 September 2018.

**Figure 20.** The graph of the time probability of landslide. The bar is the graph of the time probability of landslide in Xiji County from 8–28 September 2018. The broken line is the graph of dangerous time probability of landslide in Xiji County from 8–28 September 2018.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: figure
Figure

Image /page/16/Figure/8 description: A map displaying the geological landslide risk probability for a specific region. The map is color-coded, with a legend indicating the probability levels. A north arrow is in the top left corner. The legend, titled 'Geological Landslide Risk Probability', shows a scale from low risk (dark green) to high risk (red). The probability ranges are as follows: 0-0.024 (dark green), 0.025-0.078 (green), 0.079-0.149 (light green), 0.15-0.231 (yellow-green), 0.232-0.329 (yellow), 0.33-0.439 (light orange), 0.44-0.553 (orange), 0.554-0.675 (dark orange), 0.676-0.812 (orange-red), and 0.813-1 (red). The map is predominantly green, with patches of yellow, orange, and red indicating higher-risk areas. A single yellow dot on the map is labeled 'September 18, 2018 Landslide section'. A scale bar at the bottom right is marked in meters, with intervals at 0, 6250, 12500, and 25000.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure: Figure 21. Map of landslide hazards in Xiji County based on multi-Graded Cascade Random Forest.

Figure 21. Map of landslide hazards in Xiji County based on multi-Graded Cascade Random Forest.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: text

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In this case, since the distribution data of buildings and roads in Xiji County is included in the DPKG, it further filtered the buildings and roads in the high-risk areas of geological landslides on 18 September 2018, as shown in Figure 22. From Internet news, we learned that a landslide occurred on National Highway 309 on 18 September 2018, which caused road blockage. The actual geological landslide section is located in the dangerous road area that has been correctly predicted by our method, as shown in Figure 22. Experiments show that it successfully hit the geological landslide event within the time and space range of the warning.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure

Image /page/17/Figure/2 description: A diagram illustrating the identification and data representation of geological hazards. On the left is a map of a region with a legend indicating different features: grey for Road, pink for Building, red for Risk road, orange for Risk Building, and purple for the September 18, 2018 Landslide section. The map includes a north arrow and a scale bar from 0 to 24000 meters. A small section of the map is magnified and shown in the center as a satellite image of a winding road in a mountainous area. This image has colored overlays corresponding to the legend. To the right, two entity relationship diagrams show how these features are structured as data. An arrow from the purple landslide section points to a 'Landslide Entity' diagram with the following attributes: 'hasCenterLonLat' is '102.98438°E, 26.502315°N', 'hasTime' is '2018-09-18', and 'hasTileCode' is 'z20\_x321\_y765'. An arrow from the red road section points to a 'RiskRoad Entity' diagram with attributes: 'hasCenterLonLat' is '102.98438°E, 26.502315°N', 'hasTime' is '2018-09-18', and 'hasTileCode' is 'z20\_x321\_y766'.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure: Figure 22. Road distribution map of Xiji County included in the landslide disaster warning area on 18 September 2018.

**Figure 22.** Road distribution map of Xiji County included in the landslide disaster warning area on 18 September 2018.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
Type: text

Prediction was carried out in Xiji Country respectively for the area of 3130.00 km2, the area of 1509.54 km2, and the area of 800.88 km2. The experimental results are shown in Figure 23. With the increase of the experimental range, the time of experimental calculation also increases. The performance is basically in a satisfied performance range. Therefore, our method is applicable for large-scale disaster prediction.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure

Image /page/17/Figure/5 description: A horizontal bar chart with three bars. The vertical axis has three labels: 3130.00, 1509.54, and 800.88. The horizontal axis is labeled in minutes (min) and ranges from 0.00 to 10.00, with major gridlines at intervals of 2.00. The top bar, corresponding to 3130.00, extends to a value of 7.81. The middle bar, for 1509.54, has a value of 3.09. The bottom bar, for 800.88, has a value of 2.70. The bars are blue, with the top and bottom bars having a gradient from light to dark blue.


Document: 1. Introduction
Section: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction
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Figure: Figure 23. Comparison of forecast time for different areas in Xiji Country.

Figure 23. Comparison of forecast time for different areas in Xiji Country.


Document: 1. Introduction
Section: 4. Discussion
Type: text

Compared with the traditional prediction methods, the disaster prediction method based on the DPKG has the advantages of more accurate spatial overlay analysis, high degree of automation, and fast calculation speed, as shown in Table 2. However, it cannot be ignored that the proposed method in this paper still has certain limitations: (1) Before forecasting the target area, the data required for disaster prediction needs to be preprocessed and stored in advance, and data storage requires a certain amount of time. The larger the area, the more data dimensions required for prediction, and the longer the time. (2) The data used in this case is not accurate enough. For example, the spatial resolution of terrain

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data is 30 m, and the spatial resolution of surface coverage data for disaster prediction is 10 m. The accuracy still needs to be improved. If higher-resolution data is obtained, and more detailed tree species, tree age, and other data can be involved in disaster prediction, the accuracy of disaster prediction can be further improved. (3) The comparison with traditional disaster forecasting methods is not sufficient in the experiment that only considers the most well-known tool ArcGIS.


Document: 1. Introduction
Section: 5. Conclusions
Type: text

Compared with plain text data and structured databases, DPKG has the advantages of richer semantic representation, more accurate data content, and better query performance. The knowledge graph provides a new means for the organization, management, fusion, and analysis of multi-source heterogeneous data in complex disaster environments. It facilitates prediction, prevention, and mitigation of the natural disasters. This paper proposes the DPKG that integrates multi-source remote sensing information in disaster scenes with other multi-source heterogeneous information. It considers not only dynamic data describing spatio-temporal facts but also domain analysis models. Based on the analysis of disaster influencing factors, this paper proposes a common disaster prediction method based on the DPKG for the dynamic data-driven disaster prediction. This paper introduces new ideas by taking forest fires and geological landslides as examples. The proposed method can serve the research and application of disaster emergency response with the strong help of artificial intelligence technology.

- (1) From the perspective of cross-domain knowledge integration, knowledge graph integrates remote sensing knowledge and expert knowledge through semantic technology. It effectively connects multi-source heterogeneous data (GIS, meteorology, terrain, ground sensors, etc.) with expert knowledge in the field of disasters;
- (2) The DPKG contains dynamically updated spatio-temporal facts that reflect changes in the real world. Through knowledge graph query, the query performance in disaster emergency scenarios can be improved, and dynamic data updates can automatically drive prediction;
- (3) It provides efficient data storage and management methods for practitioners in the fields of remote sensing and geo-information, which helps to improve the efficiency

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of spatio-temporal data query. It reduces the manual effort by using reasoning of the knowledge graph.

The DPKG proposed in this paper aims at the integration of remote sensing information, geographic information, and the correlation between dynamic data and static knowledge in complex disaster environments. It lays the foundation for future in-depth research, particularly for knowledge extraction and knowledge discovery for disaster prediction. In future work, indexing and query performance need to be further verified in the case of more types and larger amounts of data. The depth of knowledge reasoning needs to be further improved. In future research, we will enhance the DPKG by leveraging graph neural networks and deep learning models, so as to integrate a large number of spatio-temporal facts, and realize disaster emergency decision-making, historical data verification, and new model construction; it needs to build a knowledge service system for natural disaster monitoring and prediction as well as the emergency decision-making. In addition, this article basically focuses on verifying the availability and usability of our model through case studies. We will compare with more traditional prediction methods in the future

**Author Contributions:** Conceptualization, X.G. and Y.Y.; methodology, X.G. and W.L.; validation, X.G., W.L. and W.Z.; resources, W.L.; data curation, X.G. and W.L.; writing—original draft preparation, X.G. and J.C.; writing—review and editing, X.G., Y.Y., L.P. and Z.H.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Beijing Municipal Science and Technology Project (Z191100001419002), and Ningxia Key R&D Program (2020BFG02013).


Document: 1. Introduction
Section: 5. Conclusions
Type: text

. Z . ; resources , W . L . ; data curation , X . G . and W . L . ; writing — original draft preparation , X . G . and J . C . ; writing — review and editing , X . G . , Y . Y . , L . P . and Z . H . ; funding acquisition , L . P . All authors have read and agreed to the published version of the manuscript . * * Funding : * * This work was supported by the Beijing Municipal Science and Technology Project ( Z191100001419002 ) , and Ningxia Key R & D Program ( 2020BFG02013 ) .

**Institutional Review Board Statement:** Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: Data sharing not applicable.

Conflicts of Interest: The authors declare no conflict of interest.


Document: Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)
Type: text

Abstract: "Glacial Lake Outburst Floods (GLOFs)" are sudden, high-magnitude floods resulting from the breach of natural dams containing glacial lakes. With climate change accelerating glacial lake formation, GLOFs present growing threats to downstream communities and infrastructure. This study introduces an AIpowered chatbot system designed for real-time GLOF risk prediction and assessment. By leveraging predictive models such as ARIMA, GRU, and LSTM, the system analyses user-provided environmental data, including temperature and water levels, to generate actionable insights. A simple and intuitive chatbot interface allows users to input key parameters, which are processed using predefined thresholds to categorize risk levels as high, moderate, or low. The system enhances disaster preparedness and management through accessible and timely decision support. Future work aims to integrate IoT-enabled realtime data streams to improve prediction accuracy further, making this solution a scalable and effective tool for mitigating GLOF risks in vulnerable regions.

Keywords: GLOFs, AI-Powered chat bot, Predictive models.

data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md


Document: 1. Introduction
Section: 1. Introduction
Type: text

In recent years, advancements in artificial intelligence (AI) and predictive analytics have significantly transformed disaster risk management. Among these, the prediction of Glacial Lake Outburst Floods (GLOFs) has garnered attention due to their catastrophic impact on downstream communities and infrastructure. With climate change accelerating glacial lake formation, GLOF prediction has become increasingly vital. However, the task is challenging due to the dynamic nature of glacial systems, limited real-time data, and the complex interplay of environmental factors.

To address these challenges, AI-powered chatbot systems have emerged as an innovative solution. By leveraging pretrained predictive models like ARIMA, GRU, and LSTM, these systems analyze environmental data such as temperature and water levels to predict GLOF risks. This approach reduces computational complexity, ensures real-time responsiveness, and provides accurate risk assessments. Additionally, the chatbot interface enhances accessibility, allowing non-experts to receive actionable insights for disaster preparedness and management.


Document: 1. Introduction
Section: 2. Literature Review > A. Traditional Methods
Type: text

Traditional methods for GLOF prediction primarily relied on field surveys, remote sensing, and hydrological modelling. While effective for historical assessments, these methods lacked real-time responsiveness and required significant expertise and resources. Additionally, they were unable to account for rapidly changing environmental conditions, limiting their predictive accuracy in dynamic glacial systems.

2. Literature Review
A. Traditional Methods


Document: 1. Introduction
Section: 2. Literature Review > B. Environmental Data Analysis
Type: text

Environmental data analysis plays a crucial role in GLOF prediction. Early approaches focused on analyzing individual factors, such as glacial lake volumes or temperature trends, but failed to integrate multiple parameters effectively. Recent advancements in machine learning models like ARIMA and GRU have enabled the analysis of complex, multivariate datasets. These models can identify temporal patterns and interactions between variables, significantly improving prediction accuracy and reliability.

2. Literature Review
B. Environmental Data Analysis


Document: 1. Introduction
Section: 2. Literature Review > C. AI and Chatbot Systems
Type: text

The integration of AI-powered chatbots has revolutionized the accessibility and usability of early-warning systems. Predictive models, including LSTM, have been incorporated to analyze real-time data and provide accurate risk categorizations. Chatbots offer a user-friendly interface, allowing stakeholders to input environmental parameters and receive actionable insights instantly. This eliminates the need for specialized knowledge and reduces reliance on computationally expensive systems. Future advancements include integrating IoT sensors for continuous data collection and enhancing prediction accuracy through real-time feedback loops.

2. Literature Review
C. AI and Chatbot Systems


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing
Type: text

The dataset utilized for this project includes historical and real-time environmental data sourced from public repositories, including satellite imagery, hydrological datasets, and meteorological records. Key parameters such as temperature, precipitation, and water levels were analyzed to ensure

<sup>\*</sup>Corresponding author: harrini.d.s@gmail.com

accuracy and consistency. Preprocessing steps involved normalizing data to handle varying units and filling missing values using interpolation techniques. Data augmentation, such as synthetic generation of extreme scenarios, was employed to improve the robustness of predictive models. The dataset was split into training, validation, and testing sets in a 70:20:10 ratio for optimal model evaluation.


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing > A. Model Development
Type: text

Predictive models, including ARIMA, GRU, and LSTM, were employed for analyzing temporal and multivariate data. These models were selected for their ability to capture timeseries patterns and long-term dependencies. The final predictive layers were tailored to classify GLOF risk into high, moderate, or low categories. A threshold-based logic system was integrated to interpret model outputs into actionable risk levels.


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing > B. Training and Validation
Type: text

The models were trained using the Adam optimizer and mean squared error (MSE) loss function. Early stopping was implemented to avoid overfitting, and learning rate scheduling was applied to enhance convergence. The training process was monitored using validation metrics, including root mean square error (RMSE) and accuracy. Augmented scenarios in the training data ensured better generalization for unseen conditions.


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing > C. Testing and Evaluation
Type: text

The trained models were tested on a holdout test set comprising unseen environmental data. Evaluation metrics such as RMSE, accuracy, precision, and recall were computed to assess the system's predictive performance. Misclassified or ambiguous cases were analyzed to refine model thresholds and improve overall reliability.


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing > D. Deployment
Type: text

The predictive system was deployed as an AI-powered chatbot application. The chatbot interface, built using Python's Flask or FastAPI frameworks, allows users to input environmental data such as temperature and water levels. Based on these inputs, the chatbot provides real-time GLOF risk categorization and actionable recommendations. The frontend offers an intuitive chat container to ensure accessibility for users without technical expertise.


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing > D. Deployment
Type: figure
Figure

Image /page/1/Figure/12 description: A flowchart, labeled "Fig. 1. Flow diagram," illustrating a machine learning workflow. The process flows downwards through several stages, each represented in a separate horizontal section on a light blue background. The stages are: 1. Data Collection & Preprocessing, 2. Model Development, 3. Training & Validation, 4. Testing & Evaluation, and 5. Deployment. The final stage, Deployment, branches out to two components: "User Input (Temperature, Water Level)" and "Backend Prediction (Flood Risk)."


Document: 1. Introduction
Section: 3. Methodology Data Collection and Preprocessing > D. Deployment
Type: text

Fig. 1. Flow diagram


Document: 1. Introduction
Section: 4. Results
Type: text

The early prediction system for Glacial Lake Outburst Floods (GLOFs), utilizing advanced machine learning models like LSTM and GRU, demonstrated significant predictive accuracy, achieving an RMSE of approximately 0.85 and an accuracy of 88% on the test dataset. The models effectively captured temporal patterns in environmental data, including temperature, precipitation, and water levels, generalizing well to unseen scenarios. Training metrics, including validation loss and accuracy, showed consistent improvement, indicating robust model learning.


Document: 1. Introduction
Section: 5. Conclusion
Type: text

In this project, we successfully developed an early prediction system for Glacial Lake Outburst Floods (GLOFs) using advanced machine learning models integrated into a chatbot interface. The predictive models, including LSTM and GRU, were trained on environmental datasets comprising parameters like temperature, precipitation, and water levels. By leveraging pre-trained models and fine-tuning them for GLOF prediction, the system achieved high accuracy and reduced computational overhead compared to traditional approaches.

The chatbot provided a user-friendly platform for real-time risk assessment, allowing non-expert users to input data and receive actionable insights. While the system demonstrated strong performance in categorizing risk levels, slight inaccuracies were observed in cases with overlapping environmental thresholds, reflecting the inherent complexity of GLOF prediction. Future enhancements, such as incorporating IoT-based real-time data, are planned to further improve prediction accuracy and system reliability.


Document: Early Warning System for Glacial Lake Outburst Using Multi Source Data
Type: text

Abstract—Glacial Lake Outburst Floods (GLOFs) pose serious risks to ecosystems, infrastructure, and communities downstream. By combining several data sources, such as satellite imaging, remote sensing, hydrometeorological data, and real-time sensor networks, this study presents a unique early warning system (EWS) intended to mitigate these hazards. The EWS can monitor and forecast GLOF incidents more accurately and quickly by using machine learning algorithms and predictive models, which will improve preparedness and mitigation activities in areas that are at risk. The system continuously monitors glacial lakes using satellite imaging and remote sensing, gathering vital information on the dynamics of the lakes and the surrounding ecosystem. This data is paired with hydrometeorological data gathered from real-time sensor networks and ground-based stations, including temperature variations and precipitation levels.

Index Terms—Glacial Lake Outburst, Early Warning System, climate change, Multi source data


Document: I. INTRODUCTION
Section: I. INTRODUCTION
Type: text

Global glaciers are rapidly retreating as a result of the accelerating speed of climate change. As a result of this process, glacial lakes have formed and grown, frequently being blocked by loosely consolidated moraines or unstable ice. Glacial lake outburst floods, or GLOFs, are caused by abrupt breaches of these natural dams. GLOFs can release enormous amounts of water in a matter of minutes and are distinguished

by their great size and abrupt onset. These floods can have disastrous aftereffects, resulting in a large number of fatalities, extensive property damage, and extensive harm to downstream communities' infrastructure and natural environments.

The current techniques for tracking glacial lakes and fore-casting GLOFs are frequently constrained by their need for recurring field surveys. The world's glaciers are melting at previously unheard-of speeds as a result of climate change. The creation and growth of glacial lakes as a result of this melting presents a serious risk of glacial lake outburst floods (GLOFs). When the water in a glacial lake is released, usually as a result of the lake's natural dam collapsing or other precipitating events like landslides or ice avalanches, GLOFs—sudden, high-magnitude floods—occur. These floods can have disastrous effects, including downstream agricultural and natural landscape degradation, infrastructure damage, and fatalities.

Due to traditional monitoring methods not being able to provide real-time data or adequate warning signals, anticipating GLOFs in glacial lakes has proven to be incredibly difficult.

The retreat and melt of glaciers across the globe is at its peak, and all this is mainly due to an increase in global temperature owing to climate change. Unstable glacial lakes have started to appear in various mountainous regions due to this change. These lakes are often obstructed by loose rocks

or ice moraines, making them incredibly prone to surprising breaches. Such breaches can lead to glacial lake outburst floods (GLOFs) which unleash tremendous amounts of water and debris in a short period of time. Such phenomena put a lot of downstream communities at risk along with causing a lot of destruction to several ecosystems, infrastructural buildings, and even agricultural lands. These GLOFs are expected to occur more frequently than they already do, putting the atrisk populations in danger.


Document: I. INTRODUCTION
Section: II. BACKGROUND
Type: text

Glacial lake outburst floods (GLOFs) are characterized by the rapid and sudden release of moisture from glacial lakes, formed as glaciers melt and retreat. Usually, ice or moraines, which are naturally unstable and prone to fracturing, act as barriers to keep these lakes back. including breaches can be caused by a wide range of events such as landslides, seismic activity, extreme rainfall, or the gradual warming of ice dams brought on by warming global temperatures. Particularly in high-altitude areas like the Himalayas, the Andes, and the Alps, the number of glacial lakes has increased as a result of the glaciers' accelerated retreat due to ongoing climate change.

GLOF prediction and prevention has always been a major challenge due to the flexible and changing complexity of glacial lake systems at a multi-level throughout history. While infrequent field surveys and remote sensing are some of the lake monitoring techniques, they are not capable of collecting data in real time and capturing the fast changes in the environment rendering them ineffective. The alarming systems set up to signal GLOF has been mostly late or ineffective which catastrophically impacts the communities located downstream. The advancements in machine learning, remote sensing, sensor networks and satellite technology have provided an opportunity to develop more efficient real time GLOF monitoring systems. By combining data from different sources such as satellite images, hydrometeorological data and groundbased sensors, an integrated system enables efficient monitoring of glacial lakes. This approach is enhanced even further through the use of machine learning algorithms which are able to analyze the data and detect early indicators, making the alarms timelier.


Document: I. INTRODUCTION
Section: III. LITERATURE SURVEY > A. Glacial Lake Formation and GLOF Events
Type: text

The impact and frequency of Glacial Lake Outburst Floods (GLOFs) has been a concerning factor on a global scale especially in the higher altitudinal belts which are witnessing accelerated global warming which in turn is causing drastic changes in glaciers. Places like the Himalayas, Andes, Alps and Rockies are particularly vulnerable to such events. Various researchers have studied the processes involved such as J.S. Kargel, U.K. Haritashya [1] highlighted the reasons behind the formation of glacial lakes and the various reasons that act as triggers for GLOFs. Their investigations

reveal the glacier boundary destabilization factors such water in the form of ice avalanches, precipitation, earthquakes and other volcanic activities. Such factors would create a certain level of chaos which would lead to terrifying bursts endangering the regions prone downstream.


Document: I. INTRODUCTION
Section: III. LITERATURE SURVEY > B. Traditional Monitoring Methods and Limitations
Type: text

Over the last few years, satellite monitoring of glacial lakes has increased due to the ability to produce higher resolution data and cover wider areas compared to traditional groundbased methods. For instance, X. Wang, X. Guo, C. Yang [2] examined the accuracy of monitoring glacier lakes with satellite imagery. One major disadvantage of satellite monitoring is that it is not as frequent as the events it is trying to capture, thus it may not capture the rapid and highly dynamic changes in the lake glacier's conditions at a time, despite the advantages that come with it. These weaknesses show that there is a requirement for an ongoing, more holistic monitoring system that can offer real time information and early warnings. A more sophisticated early warning system would significantly enhance the identification and forecasting of GLOF events with the help of advancements in satellite technology, remote sensing, and sensor networks, as well as the analytical capabilities of machine learning algorithms.


Document: I. INTRODUCTION
Section: III. LITERATURE SURVEY > C. Advances in Remote sensing for Glacial Monitoring
Type: text

Better and continuous monitoring of glacier lakes is now possible due to recent improvements in satellite technology and remote sensing capabilities. In their study, for instance, G. Zhang, T. Yao, H. Xie [3] employed high- resolution satellite data from MODIS, Sentinel-2, and Landsat to inform their work. Glacial lakes' growth and volume changes have been effectively monitored and charted in satellite data studies, proving the effectiveness of remote sensing technology in pattern and trend identification. This paper highlights how data from these cutting-edge satellite images can be used by Glacial Lake Outburst Flood (GLOF) Early Warning Systems (EWS). Through the collection of wide-ranging, high-quality lake images, these platforms deliver valuable information on the shifting processes and ecological status of glacier lakes.


Document: I. INTRODUCTION
Section: III. LITERATURE SURVEY > D. Multisource Data Integration for Real-time Monitoring
Type: text

Ground-based sensors provide timely measurements of physical attributes such as temperature, precipitation and seis mic activity, while high resolution satellite imagery allows for continuous monitoring of glacial lake dynamics [4]. This multifaceted approach, in addition to enhancing the capacity to detect early signs of potential GLOF events improves the overall reliability of forecasts. However, these benefits do not come without a number of logistical and technical challenges. The two major obstacles in this regard are merging numerous data sources, and it demands clever data processing


Document: I. INTRODUCTION
Section: knowledge to ensure seamless workflow [5]. > E. Case studies and Early warning system Implementations
Type: text

Several case studies have successfully implemented earlywarning systems for Glacial Lake Outburst Floods (GLOFs), which have shown the potential benefits of GLOFs early warning systems. The International Centre for Integrated Mountain Development (ICIMOD) has piloted early warning systems that leverage satellite data, remote sensing and community- based monitoring systems to issue timely alarms, in the Hi- malayas. This integrated approach not only improves the accurate prediction but also ensures active participation of the community in monitoring the system, which makes people more prepared and provides a sense of ownership. Work like this has been done in the Fan, X., Xu, Q., van Westen, C.J., Huang. (2008) state.


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > A. Data Processing
Type: text

Make the satellite images into shapes that help objects and Track changes. This may include the use of techniques such as twitching images specific to highlight Hydrometeorological data: Mix data from weather stations and Censorship in the usual time slot to create a clear time Shows patterns over time. You can stick together and sort data from ground -based sensor to create a complete overview of the environmental conditions, Similar to the hydrometeorological data.


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > B. Feature Extraction and Analysis > Ground -based full sensor data recovery:
Type: text

To detect the symptoms of potential volatility in the lake, the sensors gathered on the ground to extract the environment and physical symptoms from the records. Earthquake hobbies, temperature races and downs are important variables required to maintain a look. Initiative warnings of instability inside Glacial Lake -Gadget may be located using journal aggregation and evaluation methods, including non -conformity detection and time collection analysis.


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > B. Feature Extraction and Analysis > Image processing:
Type: text

Satellite analysis for PC images for important features for the use of refined photo processing techniques. This involves monitoring the shift within the boundaries of the lake and detecting the variation inside the floor space of the lake, and comparing ice and ice conditions on site. To draw attention to significant differences and patterns in SNAP shots, techniques can be used with image partitions, part detection and alternative identity algorithms.

IV. METHODOLOGY
B. Feature Extraction and Analysis
Image processing:


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > C. Machine Learning Model Development > Exercise and testing:
Type: text

To create a reliable model, data must be divided into training and test kits. The model can learn and recognize high -risk signals by practicing historical GLOF events. The model is fed data during the training phase so that it can identify and absorb patterns related to previous GLOF events. To confirm the accuracy of the model after training, unseen data must be used for testing. The model's ability to extract the prophecies of the prophecies, the ability to extract for real world conditions is guaranteed by this stage. To maximize accuracy and performance, the hyperparameter may need to be replaced in this phase.

IV. METHODOLOGY
C. Machine Learning Model Development
Exercise and testing:


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > C. Machine Learning Model Development > Future modeling:
Type: text

The model can evaluate the current data in real time so that it is trained and verified to find a gloff risk pattern. Unusual temperature spikes, sudden changes in the surface of the lake, or closer to seismic activity to the lake, can be all important signs. The sustained monitoring of these parameters is performed by the model, which immediately releases the alarm, when possible, dangers are identified.

IV. METHODOLOGY
C. Machine Learning Model Development
Future modeling:


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > D. Early Warning System Implementations
Type: text

Real -time warning: Install an exact intersection points for each risk category, when the alarm is immediately triggered to warn of the importance of a potential gloff cross the significant values. For high -risk conditions, the first warning system (EWS) creates automatically alerts, which later expanded the right authorities and population nearby. This guarantees that interested parties are properly informed so that they can take the right protection measures.

Data Virtue and Dashboard: Create an interactive dashboard showing model conditions and real -time data. For each lake during observation, this gives the dashboard a variety of visualization, including time series charts, maps and risk level indicators. Straits can quickly evaluate GLOFF risk for these visual aids and evaluate them, who inform and inform decisions to reduce any effect.

Risk assessment: Determine the risk level of each monitored lake using the production from the approximate model. The model uses a mixture of historical trends and uses variable power variables to classify lakes in low, medium, or high-risk groups. The greatest risk of lakes that pose the greatest risk, it helps to determine priorities for classification resources and monitoring of activities.

IV. METHODOLOGY
D. Early Warning System Implementations


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > E. Testing and Calibration
Type: text

Field Test: Conduct field experiments of the initial warning system (EWS) by modeling multiple risk scenarios for glacier flooding (GLOF). These tests are required to confirm that

the system can predict GLoft events and provide timely alerts. Real-time simulation exercises can assess the accuracy and reaction of the system; simulation exercises can include controlled liberation, false stress tests at lake boundaries, or artificially elevated water levels.

Adjustment and calibration of the model: model: Change the parameters of the model to improve its accuracy and dependence in light of this field test results. Some important steps, including calibration, are

Refining sensor box: The sensor is sufficient to identify gloves without installing the false alarm to adjust the threshold. Change the Parameters to Machine Learning Models: Adjust the parameters of machine learning algorithms in EWS. This can change the complexity of the model, learning speed, or decision limit to increase the performance of prediction.

Changing data preparation methods: In order to guarantee the best possible processing of input, you can visit the data to pre -process processes again. To better prepare the data, they can adjust the generalization area, upgrade the noise filtering strategies, or refine the data change processes.

IV. METHODOLOGY
E. Testing and Calibration


Document: I. INTRODUCTION
Section: IV. METHODOLOGY > F. Continuous Monitoring and Improvement
Type: text

Feedback loop: Establish a method of ongoing monitoring to evaluate the first warning method (EWS) efficiency. To determine the user inputs to get the improvement needed. The accuracy and effect of the system is maintained by adjusting the future model in response to fresh data and user response. EWS can be adjusted under changing conditions and may be better over time due to this relapse method.

System upgrade: To use the latest development in technology, update regular data sources, machine learning algorithms and alarm systems. EWS skills can be improved by integrating new techniques and approaches when they become available. To guarantee reliable function, inspect and maintain regular sensors and data collection equipment. Reorganization of the sensor, updates software and confirms that each part is involved in this process is all included in this process.

EWS can be effective and adapted to the changing conditions of the glacial lake isos system by implementing a strong feedback loop and system improvement.


Document: I. INTRODUCTION
Section: V. CONSTRUCTION OF CIRENMACO EWS
Type: text

To take advantage of the latest technology developments, update data sources, machine learning algorithms and

alarm systems regularly. EWS capabilities can be improved by integrating new technologies and approaches as they are available. To ensure reliable operation, inspect and maintain data collection sensors and equipment regularly. Recalibration sensors, updating the software and confirming that all parties are operating as intended are all included in this process.

EWS can remain efficient and adaptable to the conditions of change of lake's glacial ecosystems, implementing a strong feedback cycle and system improvements. This strategy helps to reduce hazards related to Glacial Lake Outburst Floods (GLOFs) by guaranteeing that the system will continue to issue precise and timely warnings. After an assessment of the vulnerability of the lake, the EWS design phase focuses on building the required infrastructure. This involves setting up a communication network around high-risk regions and strategically placing sensors. Among the key sensors used are: water level sensors: These provide vital information on growing or declining water volumes by tracking variations in the lake's water levels. seismic sensors: They identify soil movement that can indicate potential triggers such as earthquakes or landslides.

Meteorological stations: By monitoring changes in temperature and rainfall, these stations help determine environmental factors that can cause gloff.

Flow Sensor: These downstream sensors keep an eye on the discharge of unusual water that may indicate a adjacent explosion. To ensure real-time data transfer in a central control center, all these sensors are associated with a reliable communication network, which often uses satellite technologies or GSM. Continuous monitoring and quick notifications are possible by this configuration, which is necessary to reduce the risks associated with the glof and improve the security of downstream communities. Data processing and integrations come after the infrastructure setup. Important information about lake levels, seismic activity and weather is shown on a real -time dashboard that tracks the sensor data in a centralized database. While the risk thresholds are set for critical matrix, machine learning algorithms examine the data to find the pattern to find the risk to explore that gloff events. If sensor data exceeds specified levels, EWS automatically triggers alerts and notifications. These signs enable emergency services, local communities, and relevant officers to prepare and react quickly. This method helps communities to be informed and flexible against potential flood hazards in the Cirenmaco region.


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > A. Software Implementation
Type: text

The main goal of real -time monitoring, data processing, predictive modeling, and alert generation is the main goal of a reliable, scalable and user -friendly platform software

VI. IMPLEMENTATION
A. Software Implementation


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > A. Software Implementation
Type: figure
Figure

Image /page/4/Figure/1 description: A figure containing a map labeled 'A' and an inset bar chart labeled 'B'. The map shows the GLOF (Glacial Lake Outburst Flood) susceptibility of a mountainous region. A legend in the bottom left defines the susceptibility levels with colored dots: Very low (white), Low (light green), Medium (yellow), High (blue), and Very high (red). These dots are plotted across the topographical map, which displays snow-covered peaks in light blue and lower elevations in brown and green. Several specific locations are labeled, such as M20, M24, M47, and M51. The map includes a 10 km scale bar and coordinate markings. The inset bar chart 'B' shows the count of lakes versus their size-interval in square kilometers. The x-axis shows size intervals from 0.05-0.1 to >0.5 km². The y-axis shows the count, up to 30. The bars are stacked and colored according to the susceptibility legend, indicating that the most numerous lakes are in the smallest size category (0.05-0.1 km²) and have low susceptibility, while a large portion of the biggest lakes (>0.5 km²) have very high susceptibility.


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > A. Software Implementation
Type: text

Fig. 1. —(A) spatial distribution of 64 glacial lakes assessed with their GLOF susceptibility classes across the Mahalangur Himalaya. (B) The bar graph shows the GLOF susceptibility of glacial lakes in various size interval [13].

project that applies CIRENMACO Early Warning System (EWS) for Glascable Lake Outburst Flood (Glofs). Use a trusted cloud-based solution, such as Google Cloud platform or AWS (Amazon web services), to effectively manage the processing and storage of large-scale real-time data. For managing sensors data and historical records, cloud solution provides scalability, safety and reliable performance in Fig 1. Set the risk threshold for important matrix. The system should automatically sound alarm and inform the proper authorities, nearby communities and emergency services when the threshold is crossed. For automated alert delivery, use services such as AWS SNS (simple notification service) or comparable solution.


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > B. Frontend Development
Type: text

Design of user interface (UI): Provide a simple, easy-to- use interface that shows major data visualizations, such as historical trends, danger conditions and changes in the level of lake. Users should be able to interact with various charts and map-based visualizations, view real-time data and track the level of risk through user interfaces.

Dashboard features:

- Create a dashboard that allows users to see real -time sensor data classified by seismic activity and water level.
- Get historical data to analyze trends.
- Check the color-coded risk map of the item lake area.
- When the thresholds cross, get information and alert.

VI. IMPLEMENTATION
B. Frontend Development


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > C. Data Processing and Preprocessing
Type: text

Data normalization and cleaning: As the system comes into the system, put the data in procedures to validate, clean and standardize. This involves managing outlier or missing

VI. IMPLEMENTATION
C. Data Processing and Preprocessing


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > C. Data Processing and Preprocessing
Type: figure
Figure

Image /page/4/Figure/13 description: The image displays two stacked bar charts, labeled A and B, analyzing different types of lakes based on their size. The legend identifies four types of lakes by color: green for Supraglacial lakes, orange for Proglacial lakes, purple for Unconnected glacier-fed lakes, and yellow for Non-glacier-fed lakes. The x-axis for both charts is labeled "Size-interval (km²)" and has six categories: 0.001-0.002, 0.002-0.01, 0.01-0.02, 0.02-0.1, 0.1-0.5, and >0.5.


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > C. Data Processing and Preprocessing
Type: text

Chart A, on the left, shows the "Number" of lakes on its y-axis, which ranges from 0 to 140. The highest number of lakes, approximately 125, is found in the 0.002-0.01 km² size interval. The number of lakes generally decreases for larger size intervals, with the >0.5 km² category having fewer than 10 lakes.

Chart B, on the right, shows the total "Area (km²)" of the lakes on its y-axis, which ranges from 0 to 8. In contrast to the number of lakes, the total area generally increases with the size interval. The largest total area, approximately 7.5 km², is contributed by lakes in the >0.5 km² size category, which are predominantly proglacial and unconnected glacier-fed lakes. Proglacial lakes contribute the most to the total area in the larger size intervals (0.1-0.5 and >0.5 km²).

Fig. 2. The frequency and area of glacial lakes according to different size interval and types [14].

values, ensuring that the measurement units are consistent, and convert data into a format that is compatible.

Adapt the storage and recovery by Fig.2 structuring the database with effective sequencing to provide quick data recovery, which is necessary for real -time, mass dataset analysis.

To install the necessary equipment in high -risk areas, the process begins with the initial fieldwork. During this phase, Cirenmaco Lake is surrounded with carefully downstream flow sensors, meteorological stations, seismic sensors and water level sensors. It asks for reliable equipment that can avoid challenging environmental conditions, as well as trained workers to guarantee ideal sensor placements and accuracy. Each sensor is connected to a reliable communication network and is set to collect data continuously. Typically, satellite or GSM technology is employed, providing a reliable link to send data to the Central Monitoring Center in real time.


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > D. Co-ordination and Integration of data
Type: text

A centralized data receives the sections of repository data that collect from the sensor. To maintain stability between sources, data is processed, cleaned and normalized here. After that, the dashboard has been configured for real -time monitoring, showing important data metrics and visualizations that help analysts track signs of lake stability, such as earthquake activity and variations in the water level. At this point, the system is calibrated to identify abnormalities that can point to potential glof events, and baseline data is collected to understand specific trends.


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > E. Using Machine Learning
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A comprehensive strategy that mixes machine learning for future stating analysis with real -time environmental data collection is required to apply Cirenmaco Early Warning System (EWS) for Glassel Lake Outbst Floods (Glofs) as a software project. Designing system architecture is the first stage, and data processing and storage are controlled by cloud-based services such as AWS or Google Cloud. A streaming pipeline (such as Apache Kafka) is used to manage the import of real -time data. This pipeline collects data from the sensor that monitors weather, seismic activity and water

level. After that, this data is pre -evacted using procedures to clean, normally and format it to guarantee accuracy. Other time-series features are developed to catch temporary patterns, and major features-as temperature fluctuations and water level trends-are chosen based on their importance for glof risk.

Algorithm Selection: Classify risk levels using past GLOF data by using supervised learning techniques like Random Forest or Support Vector Machine (SVM). Potential trends in water levels or other indicators can be predicted using timeseries analysis (e.g., LSTM neural networks).

Training and Testing Models: Create and train a machine learning model to identify risk indicators using historical data, then assess the model's accuracy using a testing dataset. On the basis of incoming data, the trained model may then forecast future GLOF hazards.

Backend integration: Put the learned model into practice in the backend, where it may process sensor data in real time and produce a prediction or danger level.

VI. IMPLEMENTATION
E. Using Machine Learning


Document: I. INTRODUCTION
Section: VI. IMPLEMENTATION > F. Alert and Notification System
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The Cirenmaco Early Warning System's (EWS) warning and notification system is made to give stakeholders quick and accurate alerts so they can respond to the hazards of glacial lake outburst floods (GLOFs). When crucial thresholds are crossed, this system sends out alarms. It does this by continuously monitoring environmental data, including temperature, water levels, and seismic activity. In order to provide a progressive reaction that matches the intensity of the detected risk, the alert system is set up with risk levels—typically low, moderate, high, and emergency—that correlate to various alert circumstances.

The system automatically sends out messages across a variety of channels to important stakeholders when sensor data shows that risk thresholds are reached. Local authorities, emergency responders, and community leaders receive notifications through push notifications on mobile apps, email, and SMS. The solution guarantees prompt and effective notification distribution by utilizing APIs such as Firebase Cloud Messaging for mobile app alerts, SendGrid for emails, and Twilio for SMS. In the event of a severe emergency, the warning system also enables automatic voice calls to high-priority receivers, guaranteeing that vital information reaches them right away.

VI. IMPLEMENTATION
F. Alert and Notification System


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE
Type: text

The Early Warning System (EWS) for Glacial Lake Outburst Floods (GLOFs) combines multi-channel alerting mechanisms, machine learning-based risk prediction, and real-time data collection. By utilizing cloud computing, IoT devices, and powerful analytics, the design guarantees dependable and scalable operations. The architecture is

broken down in detail below:


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > A. Data Collection Layer:
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IoT devices and sensors:

- Real-time data is gathered by sensors positioned throughout the glacial lake, including
- Water levels and flow rates are examples of hydrological data
- Weather information: humidity, precipitation, and temperature.
- Seismic Information: Possible landslide activity and ground vibrations.
- Wireless communication technologies, such as LoRa, Zigbee, or GSM, are used to transfer data.
- Remote sensing and satellite data: lake surface area, changes over time, and snow/ice melt patterns are all provided by satellite imagery (e.g., from Landsat, Sentinel).

VII. SYSTEM ARCHITECTURE
A. Data Collection Layer:


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > B. Data Transmission and Integration Layer: > Edge Technology:
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Sensor data preprocessing and short-term storage are managed by IoT gateways at the data collection location. This guarantees that there is less data loss when being transmitted.


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > B. Data Transmission and Integration Layer: > Cloud-Based Communication:
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Protocols like HTTPS and MQTT are used to safely transfer data to a central cloud-based system. Redundancy measures are put in place to guarantee dependability and uninterrupted functioning. This configuration increases the early warning system's overall efficacy by enabling safe and effective real-time data transmission.


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > C. Data Storage Layer:
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To ensure minimal loss during transmission, edge devices—like IoT gateways at the data collection site—preprocess and temporarily store sensor data. Protocols like HTTPS and MQTT are used to securely transfer the data to a central cloud-based system, with redundancy mechanisms in place for dependability. Real-time sensor data and historical datasets for analysis and machine learning model training are stored on centralized storage platforms such as AWS, Azure, or Google Cloud. Structured data is managed by SQL databases (PostgreSQL), whereas unstructured or semi-structured data is handled by NoSQL databases (MongoDB). In order to prevent data loss and guarantee data durability and availability for long-term storage, periodic backups are kept in cold storage systems such as Amazon S3 Glacier. This configuration facilitates secure, scalable, and effective data handling.

VII. SYSTEM ARCHITECTURE
C. Data Storage Layer:


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > D. Data Processing and Analytical Layer:
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The preprocessing engine handles missing values, eliminates outliers, and arranges data for analysis by cleaning, validating, and normalizing raw sensor data to guarantee accuracy and consistency. In order to identify anomalous patterns in water levels or seismic activity and forecast the probability of a Glacial Lake Outburst Flood (GLOF) catastrophe, machine learning models such as LSTM and Random Forest examine both historical and current data. The risk assessment module uses time-series analysis and dynamic updates to classify risk levels—low, medium, high, and critical—based on machine learning outputs and predetermined thresholds. Vulnerable communities' safety and readiness are improved by this configuration, which guarantees effective and precise detection and prediction of GLOF events, facilitating prompt risk assessment and response.


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > E. User Interface and Visualization Layer:
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The dashboard interface provides real-time data visualizations to stakeholders, including academics and authorities. This comprises geographic maps showing the locations of lakes and the corresponding danger ratings, as well as trends from sensor data, machine learning forecasts, and alarms. Easy access is ensured by the interface's compatibility with mobile applications and online browsers.

An end-user-friendly interface for seeing alerts and recommendations is offered by the mobile app and notifications. For important alerts, users receive push notifications, guaranteeing prompt information and action. With this configuration, end users and stakeholders are always informed and able to take prompt action to reduce risks.


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > F. Alert and Notification Layer:
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Notifications are sent by the automatic alarm system when risk thresholds are surpassed. It makes use of many notification channels, such as push notifications (using Firebase Cloud Messaging), emails (using SendGrid or AWS SES), and SMS (using Twilio or comparable APIs). Alerts give information about the location, level of risk, and suggested courses of action. The system initiates extra actions, such as automated voice calls or direct integration with disaster management systems, in response to high-priority alarms, including impending GLOFs. This all-inclusive alarm system guarantees prompt and efficient communication to the appropriate authorities and local communities, improving their capacity to react quickly and reduce hazards.

VII. SYSTEM ARCHITECTURE
F. Alert and Notification Layer:


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > G. Monitoring and Maintenance Layer:
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The system tracks health, uptime, and error rates using cloud monitoring technologies such as Google Cloud Monitoring and AWS CloudWatch. To maintain openness and facilitate postevent study, it records all information, forecasts, and warnings. There is a feedback loop in place, whereby new data is frequently added to machine learning models to increase their accuracy. Additionally, user input is incorporated to improve system usability and alert thresholds. In order to successfully enable real-time monitoring, data processing, predictive modeling, and warning generation for glacial lake outburst floods (GLOFs), this all-encompassing methodology guarantees the system's dependability, transparency, and continual improvement.

VII. SYSTEM ARCHITECTURE
G. Monitoring and Maintenance Layer:


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:
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As more lakes and sensors are added, the system's scalability is built to manage growing data volumes, guaranteeing futureproofing as the project expands. Redundant systems that provide minimal downtime and continuous operation help to preserve their resilience. Access control, data encryption (SSL/TLS), and frequent security audits are used to safeguard private data from breaches and unwanted access. Because of its interoperability, the system may easily interact with other systems, such as disaster response platforms and GIS tools, to offer complete risk management. Glacial lake outburst flood (GLOF) risk monitoring and mitigation are successfully supported by the system's strong, effective, and flexible modular design in Fig 3.

VII. SYSTEM ARCHITECTURE
H. Key Features of the Architecture:


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:
Type: figure
Figure

Image /page/6/Figure/12 description: A flowchart illustrating the methodology for assessing Glacial Lake Outburst Flood (GLOF) susceptibility. The process begins with 'Satellite imagery', which is used for 'Digitization' to create a 'Glacial Lake Inventory'. A decision diamond then checks if the 'Lake area' is greater than or equal to 0.05 km². If 'No', the lake is 'Excluded'. If 'Yes', it is 'Included for further analysis'. This analysis involves 'Factors computation & setting criteria (Index value)', which uses 'Lake Glacier Watershed DEM' as an input. Parallel to this, factors are identified, selected, and defined (F1, F2, F3, F4, F5, F6). Another input, 'AHP (determination of factor weights)', provides factor weights. The next step is the 'Multiplication of index value with factor weight', followed by the 'Sum of Scores'. This sum leads to the final 'GLOF Susceptibility' assessment. The susceptibility is categorized into five levels, each with a corresponding score range and color: 'Very Low (0.25-0.40)' in green, 'Low (0.40-0.55)' in light green, 'Medium (0.55-0.70)' in yellow, 'High (0.70-0.85)' in blue, and 'Very High (≥0.85)' in red.


Document: I. INTRODUCTION
Section: VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:
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Fig. 3. Flow chart for GLOF [15]


Document: I. INTRODUCTION
Section: VIII. EXPLANATION OF FLOW CHART
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Satellite photos: The first step in the procedure is gathering high-resolution satellite photos of glacier lakes and the areas around them.

Digitization: A Glacial Lake Inventory is produced by digitizing these satellite photos. In this process, the visual data is transformed into an analyze-able digital format.

Glacial Lake Inventory: Using the digitized data, a list of all the glacial lakes found in the satellite photos is created.

Lake Area 0.05 km 2: The surface area of the lakes is used to filter them out of the inventory. For additional analysis, only lakes having an area of at least 0.05 km2 are taken into account.

Factor Identification: Choose pertinent geological and environmental elements.

Factor Computation: To calculate factor values and establish criteria, use data from Digital Elevation Models (DEMs).

AHP Analysis: Apply the Analytic Hierarchy Process (AHP) to ascertain factor weights. Index values are multiplied by weights, and scores are added up.

Sort lakes according to their GLOF susceptibility levels: very low, moderate, medium, high, or very high.


Document: I. INTRODUCTION
Section: IX. DISCUSSION
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The deployment of an Early Warning System (EWS) for Glacial Lake Outburst Floods (GLOFs) powered by machine learning greatly improves catastrophe risk management in glacial areas impacted by climate change. By combining automated alarms, predictive analytics, and real-time data collection, the system enhances outburst forecasts by analyzing hydrological and environmental data using models like LSTM networks. Scalability and dependability for huge sensor and satellite image collections are guaranteed by cloud-based storage. Generalizing models to different terrains and ensuring data quality in remote places are challenges. In spite of this, the EWS has enormous potential to help sustainable development, safeguard vulnerable people, and reduce GLOF risks. With further improvements, it might serve as a template for other dangerous regions around the world.


Document: I. INTRODUCTION
Section: X. CONCLUSION
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Disaster risk management in glacial regions is revolutionized by the deployment of an Early Warning System (EWS) for Glacial Lake Outburst Floods (GLOFs) based on machine learning. The system offers a proactive way to lessen the effects of GLOF by combining automatic alerting systems, real-time data collection, and sophisticated predictive analytics. For a thorough risk assessment, it examines a variety of datasets, such as hydrological, meteorological, and seismic inputs, enabling authorities and communities to take preventative measures.

The project's approach is flexible and scalable for different glacier situations, even in the face of obstacles like data availability, model generality, and infrastructure constraints.

Multi-channel notifications and cloud computing improve reach and dependability. In addition to providing a foundation for upcoming developments in environmental monitoring and early warning systems, this initiative emphasizes the role that technology may play in mitigating climate threats.


Document: I. INTRODUCTION
Section: XI. ACKNOWLEDGEMENT
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With great appreciation, we would like to thank everyone who helped us finish this project on the Cirenmaco Early Warning System (EWS) for glacial lake outburst floods (GLOFs). We are really appreciative of the direction and assistance given by our mentors and advisors, whose knowledge and experience were crucial throughout this project.

We sincerely thank the research institutions and organizations that gave us access to vital data and necessary resources. We are particularly appreciative of the meteorological and environmental organizations' technical assistance, whose data allowed us to create a more dependable and strong early warning system. We also want to express our gratitude to our team members for their commitment, diligence, and cooperation.


Document: early warning systems for glacier lake outburst floods and debris flows
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Abstract: Climate change affects the cryosphere in a strong way. In 2012 the IPCC (IPCC, 2012) concluded that there is high confidence that changes in heat waves, glacial retreat and/or permafrost degradation will have a strong on high mountain phenomena such as slope instabilities, mass movements and glacial lake outburst floods (GLOF). The overall frequency of debris flows may decrease in absolute terms, but the magnitude of events may increase. This was concluded from an analysis of debris flow events in the past 150 years and using this information for future projections on climate change. Two case studies from China and Georgia demonstrate the evidence of ongoing climate change. In both situation early warning systems and glacier monitoring are the key for a) detailed hazard assessment, b) a longterm observation of the system development and c) the warning and alarming of a major damage potential (settlements, main touristic roads, gas pipline). While the early warning system in the Yarkant River at Kyagar glacier (Chinese Karakoram Mountains) has already been tested in a real GLOF event in 2015, the system in Georgia for huge debris flows induced by glacier collapses in the on top of Mt. Kazbeg will be installed in spring 2016. In both project GEOTEST AG and GEOPREAVENT AG build a successful joint venture; while the experienced geologists and glaciologists of Geotest assess the hazard potential and evaluate mitigation measures, Geopraevent's engineers develop, install and run the early warning system. Both systems are based on modern technologies and devices.

**Keywords:** early warning system, risk management, glacial lake outburst flood (GLOF), debris flow


Document: 1. Introduction
Section: 1. Introduction
Type: text

Early warning is the provision of timely and effective information, through identified institutions, that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response. In other words, early warning can be defined as the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss (UNISDR, 2010). The definition does not include a reference to the time scale on which a warning is given. Early Warning Systems (EWS) include a chain of concerns, namely: understanding and mapping the hazard; monitoring and forecasting impending events; processing and disseminating understandable warnings to political authorities and the population, and undertaking appropriate and timely actions in response to the warnings.

A complete and effective EWS comprises four elements, spanning knowledge of the risks faced through to preparedness to act on early warning. Failure in any one part can mean failure of the whole system. The "four elements of effective early warning systems", the early warning chain, include the development and operation of early warning systems in regard to: (a) knowledge of risks; (b) monitoring and warning services; (c) warning dissemination and communication; and (d) emergency response (CCES 2013; Sättele & Bründl, 2015). These four elements imply that early warning is based on the assessment of risk and vulnerability. Moreover, early warning should be communicated appropriately and ensure response capability of the people at risk, taking into account short and long-term measures.

Mountain systems are particularly sensitive to climate change. It is important to stress that climate change will not only adversely affect the resilience of communities, environment, urban infrastructures, transportation, energy suppliers and other sectors but also EWS. It is a common opinion that beyond the need for mitigation, adaptation to climate change is an important task. It should be highlighted here that in this context not only the exemplary listed sector above will have to adapt to climate change but also existing and planned early warning concepts need to be climate checked. The need for adapting EWS may reveal for all parts of the early warning chain.

The following two case studies from China and Georgia demonstrate the evidence of ongoing climate change. In both situation early warning systems and glacier monitoring are the key for a) detailed hazard assessment, b) a longterm observation of the system development and c) the warning and alarming of a major damage potential (settlements, main touristic roads, gas pipline).


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction
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The hazard assessment of glacier lakes at remote and high-elevation sites of the Himalaya, Karakoram and Tien Shan is difficult due to restricted field site accessibility. Yarkant river in the Chinese Karakoram (Fig. 2.1) drains an area of 50'248 km2 and ranks as number one in terms of flood frequency and damage in Xinjiang. Its glacial outburst floods, with peak discharges of up to 6'000 m3 s-1, originate from a remote ice-dammed glacier lake at 4'750 m a.s.l. in the Shaksgam valley, approx. 560 km upstream of the floodplains. The hazardous lake is impounded behind the snout of the Kyagar Glacier which blocks the riverbed of Shaksgam valley. In the past the maximum lake volume was 230 million m3. Based on the hazard assessment of Kyagar Lake and newly gained knowledge about the glacier dynamics, a sophisticated monitoring and EWS for GLOFs was successfully implemented (Fig. 2.2). The system is operational since 2012.

The project is designed to significantly reduce human and material losses through adaptation in vulnerable communities in the floodplains of glacier-fed river system, by considering long-term development of the flood hazard situation in the catchment of Yarkant River. Practical approaches to climate change adaptation have been developed: (1) establishment of an early warning system (EWS) for GLOFs, (2) risk management in the floodplain, (3) glacier and climate change monitoring.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction
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Figure

Image /page/1/Figure/7 description: A figure with three maps labeled a, b, and c, illustrating the location of the Shaksgam Valley and Kyagar Glacier. Map 'a' is a large-scale map of a region in China, showing the Tarim River, Taklamakan Desert, and mountain ranges like Tien Shan, Karakoram, and Kunlun Shan. It includes an inset map of China and highlights the Shaksgam Valley. Map 'b' is a more detailed view of the Shaksgam Valley, showing the Yarkant River, Keleqin River, and several glaciers numbered 1 through 6. A legend at the bottom identifies them as: (1) North Gasherbrum Glacier, (2) Urdok Glacier, (3) Staghar Glacier, (4) Singhi Glacier, (5) Kyagar Glacier, and (6) Shaksgam Glacier. This map also marks the Kyagar Glacier Lake and an Observation Station. Map 'c' is a close-up satellite image of the Upper Shaksgam Valley and the Kyagar Glacier, showing the location of the observation station and the fields of view for two cameras, 'cam a' and 'cam b'.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction
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Fig. 2.1: Situation of (a) Yarkant river in the Tarim basin, (b) Shaksgam valley and (c) Kyagar Glacier tongue with the location of the observation station and the indicated camera view directions of the automatic camera.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System
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Because the lake is situated in a remote area, the EWS utilized a combination of satellite remote-sensing and automatic terrestrial measurements (Fig. 2.3). Based on the DEM of the empty lake basin and periodically acquired SAR images, the evolving morphology of both lake and glacier can be observed within a time interval of 11 days. The lake volume was then calculated, and the hazard level determined and transmitted to the Chinese decision-makers. In addition to remote-sensing techniques, terrestrial solar-powered, fully automatic observation stations were installed directly at the lake and further downstream along Keleqin and Yarkant rivers at Cha Hekou and Kuluklangan (Fig. 2.1 and 2.3). The observation station close to the ice dam (Fig. 2.2) was able to take daily photographic images from different viewing angles (Fig. 2.1) and to record the local meteorological conditions (air temperature, solid and liquid precipitation, humidity, global radiation). At the two stations downstream radar sensors continuously log the water level of the rivers, and pictures of the riverbed are taken. Several times a day, all data are automatically transmitted via satellite link to a specific data portal.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System
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Figure

Image /page/2/Picture/4 description: An outdoor photograph, labeled as Fig. 2.2, shows two people performing installation work on an observation station in a mountainous, glacial environment. In the foreground, one person in a blue jacket stands on top of a grey equipment box to adjust sensors on a tall pole, which also has a solar panel attached. A second person in a bright orange jacket crouches below, working on the wiring inside the open equipment box. The station is set on a rocky, barren slope next to a small, muddy stream. In the background, the large, white Koxkar Glacier stretches across the landscape, with tall, rugged, snow-capped mountains behind it under a cloudy sky. The caption reads: "Fig. 2.2 Installation work on the observation station. The Koxkar Glacier is visible in the background."