Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake
Type: figure
Figure

Image /page/7/Picture/3 description: A Landsat TM satellite image, using a 4-3-2 band combination, shows the Ngozumpa Glacier flowing through a rugged, snow-covered mountain range. The glacier is a large, light-colored river of ice running vertically through the center of the image. Several dark blue lakes are visible in the surrounding brown and white terrain. Some of these lakes, located alongside the glacier, are outlined in purple to indicate they are "Lateral moraine-dammed lakes," as explained by a legend in the bottom left corner.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake
Type: figure
Figure: Figure 4 Lateral moraine-dammed lake

Figure 4 Lateral moraine-dammed lake

data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md

3 Classification system of glacial lake
3.2 Moraine-dammed lake
3.2.3 Moraine thaw lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake
Type: text

eliminated in the inventory of glacial lakes.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.3 Ice-blocked lake
Type: text

Whether the glacier advanced and blocked the valley or the branch glacier rapidly retreated and was separated from the main glacier, they all resulted in the water accumulation, and then formed a lake. The common feature of these lakes is that their dams are composed of glacier ice, so they can be named as ice-blocked lake. According to their forming mechanism, ice-blocked lakes can be divided into advancing glacier-blocked lake and other glacier-blocked lake. The representation of the former is Kyagar Thso Lake located in the Yarkant River basin in China (Zhang et al., 1989; Wang et al., 2009). An example of the latter is Merzbacher Lake near the South Inylchek Glacier in Kyrgyzstan (Liu et al., 1998; Shen et al., 2009). Other glacier-blocked lakes also exist in the interior of the Tibetan

3 Classification system of glacial lake
3.3 Ice-blocked lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.3 Ice-blocked lake
Type: figure
Figure

Image /page/7/Figure/8 description: A figure with two panels, (a) and (b), showing satellite imagery of glacial lakes. Panel (a) is a Landsat OLI image displaying a wide view of a mountainous, glaciated area with latitude and longitude markings from 28°10'N to 28°14'N and 86°36'E to 86°40'E. A scale bar indicates a length of 2 km. A red box highlights a specific region containing several lakes. Panel (b) is a zoomed-in Google Earth image of the highlighted area from panel (a). It provides a detailed view of different types of lakes, which are labeled with arrows. These include a large 'end moraine-dammed lake', a smaller 'lateral moraine-dammed lake', and a cluster of small lakes within a yellow dashed outline identified as 'moraine thaw lake'.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.3 Ice-blocked lake
Type: figure
Figure: Figure 5 Moraine thaw lake (water body in blue-green color within the yellow-color polygon in (b)

Figure 5 Moraine thaw lake (water body in blue-green color within the yellow-color polygon in (b)


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.3 Ice-blocked lake
Type: figure
Figure

Image /page/8/Figure/2 description: A satellite image from Landsat OLI, using a 5-4-3 band combination, displays a mountainous region with glaciers. The map is marked with longitude lines from 80°48'E to 80°54'E and latitude lines from 34°18'N to 34°20'N. A specific body of water is outlined in yellow and identified in the legend as an 'Ice-blocked lake'. A scale bar in the lower-left corner indicates a distance of 2 kilometers.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.3 Ice-blocked lake
Type: figure
Figure: Figure 6 Ice-blocked lake over the Tibetan Plateau

Figure 6 Ice-blocked lake over the Tibetan Plateau


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.3 Ice-blocked lake
Type: text

Plateau (Figure 6), which has not been previously reported. For advancing glacier-blocked lakes, they usually survived for a few months to one year affected by temperature increase in low altitude and lake water erosion (Wang *et al.*, 2009). For other glacier-blocked lakes, their evolution presented different characteristics. For example, Merzbacher Lake outburst floods mostly occurred in July – September and caused 50 GLOF events from 1932 to 1997 (Liu *et al.*, 1998), showing a life cycle of glacier-blocked lake to some extent. But for glacier-blocked lakes in the interior of the Tibetan Plateau, they are usually stable.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.4 Supraglacial lake
Type: text

Supraglacial lake refers to the water body on the surface of glacier due to different ablation. These lakes usually appear on the surface of ablation zone of debris-covered glaciers. As seen in Figure 7, there are many supraglacial lakes in Rongbuk Glacier on the northern Mount Everest, the largest of which had an area of 0.47 km² in 2016. In Tomur region of Tianshan Mountains, there are also many supraglacial lakes in some debris-covered glaciers, such as Tomur Glacier, Tugaibieliqi Glacier, Qong Terang Glacier, Koxkar Baxi Glacier, etc. When supraglacial lakes are connected with the drainage system inside the glacier, the lake water can be quickly exhausted. So supraglacial lakes change rapidly in annual and inter-annual scales. In view of the spatial resolution of Landsat TM/ETM+/OLI remote sensing images widely used, the quick variation of supraglacial lake, and being the origin of moraine-dammed lake (Richardson and Reynolds, 2000), it is suggested that the supraglacial lake with an area of above 0.02 km² can be collected in the inventory of glacial lakes. The main difference between supraglacial lake and moraine thaw lake is their locations: the former is located on the surface of glacier; the latter is located on the moraine ridge of glacier.

3 Classification system of glacial lake
3.4 Supraglacial lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.5 Subglacial lake
Type: text

The water body within the glacier can be named as subglacial lake. It had been found that there were more than 140 subglacial lakes under the Antartic ice sheet, the largest one was the Vostok Lake (Wingham *et al.*, 2006). For alpine glaciers, lakes like subglacial lake had not been reported until now. However, small supraglacial lakes possibly exist in the distribution region of maritime glaciers such as southeastern Tibetan Plateau, because the drainage

3 Classification system of glacial lake
3.5 Subglacial lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.5 Subglacial lake
Type: figure
Figure

Image /page/9/Figure/2 description: A two-panel figure displaying satellite imagery of the Rongbuk Glacier. The left panel is a wide view from a Landsat OLI, 5-4-3 band combination, showing a vast area of snow-covered mountains and glaciers. The coordinates range from 86°45'E to 87°0'E longitude and 28°0'N to 28°10'N latitude. A scale bar at the bottom indicates a length of 4 km. A red box highlights a specific area on the glacier. The right panel is a magnified view of the highlighted area, showing a close-up of a supraglacial lake. The lake is colored light blue and outlined in magenta. A legend identifies the outlined shape as a 'Supraglacial lake'. The latitude for this panel ranges from 28°5'N to 28°10'N, and a scale bar at the bottom is labeled from 0 to 500 km.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.5 Subglacial lake
Type: figure
Figure: Figure 7 Supraglacial lake

Figure 7 Supraglacial lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.5 Subglacial lake
Type: text

channels under the glacier are well developed. It is impossible to identify subglacial lake only relying on satellite remote sensing image, other instrument like ground penetrating radar (GPR) is needed. For building a complete classification system of glacial lake, subglacial lake is listed, but it can be removed in the inventory of glacial lakes based on satellite remote sensing images.


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 3 Classification system of glacial lake > 3.6 Other glacial lake
Type: text

Except for 5 classes of glacial lake mentioned above, the water body formed by landslide, rock collapse, avalanche and debris flow blocking the valley in the glaciation area is named as other glacial lake. This kind of lake belongs to dammed lake and has a great potential harm to the downstream settlements, roads, hydropower stations and other infrastructures. Therefore, it should be taken into account in the inventory of glacial lakes. The first basis for identifying the other glacial lake is its dam which can be examined from the high-resolution remote sensing images. The second is that the appearance of the other glacial lake is usually related to the earthquake, debris flow and other geologic activities, and there is an obvious change of lake size around the time. For instance, the debris flow caused by Ranzeria Co Lake outburst formed two glacial lakes in Jiali County (Sun *et al.*, 2014). The quantitative index of identifying the other glacial lake is suggested that the distance from the glacier terminus is below 10 km (Wang *et al.*, 2013; Zhang *et al.*, 2015).

3 Classification system of glacial lake
3.6 Other glacial lake


Document: Definition and classification system of glacial lake for inventory and hazards study
Section: 4 Conclusions
Type: text

As an important object of the cryospheric science, glacial lakes are not only closely related to the climate change and glacier movement, but also play a role on mountain disaster chain. Therefore, glacial lakes had been paid more and more attentions by many scientists and governments. In this paper, the definition of glacial lake was comprehensively discussed. It was noted that the definition was based on two perspectives of glaciation and glacial melt-

water. But the fuzzy of spatial and temporal information made the poor operability in the inventory of glacial lakes. In short, it is difficult to determine whether one lake is a glacial lake or not. Theoretically, a glacial lake can be defined as the natural water body formed in the depression by glaciation since the LGM. However, the controversy of glacial coverage area in the LGM and researches' knowledge deficiency of Quaternary glaciology also caused difficulties of identifying glacial lakes based on theoretical concept of glacial lake. On the consideration of the interest of glacial lakes' studies, an alternative definition of glacial lake was proposed, i.e. glacial lake could be defined as the natural water mainly supplied by modern glacial meltwater or formed in glacier moraine's depression. Meanwhile, a complete classification system of glacial lake was proposed based on its formation mechanism, topographic feature and geographical position. Glacial lakes were classified as 6 classes and 8 subclasses: glacial erosion lake (including cirque lake, glacial valley lake and other glacial erosion lake), moraine-dammed lake (including end moraine-dammed lake, lateral moraine-dammed lake and moraine thaw lake), ice-blocked lake (including advancing glacier-blocked lake and other glacier-blocked lake), supraglacial lake, subglacial lake and other glacial lake.

Although this paper attempted to provide the features or quantitative indices for identifying different types of glacial lakes using satellite remote sensing images, and emphasized that the existence of modern glaciers was the primary basis for distinguishing a glacial lake, there were still some problems in the inventory of glacial lakes. When glaciers previously existed in the First Chinese Glacier Inventory and there were glacial lakes in the downstream, if these glaciers disappeared in the Second Chinese Glacier Inventory, did these lakes belong to glacial lakes? So it is very important to ensure the consistency of subject. In addition, the quantitative indices proposed for identifying some types of glacial lakes were derived from the typical glacial lakes, and their representativeness needs to be further studied.


Document: Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices
Type: text

Abstract

This paper describes the creation and deployment of a Disaster Early Warning System (DEWS) designed to detect Glacial Lake Outburst Floods (GLOFs). This system is a Comprehensive Integrated dIsaster pRevention Configuration (CIIRC)that has been built to withstand sub-zero temperatures and harsh weather conditions while displaying durability and resilience. GLOFs pose a significant risk to communities living near glacial lakes, necessitating the use of advanced monitoring equipment. The proposed system may transfer crucial data in real time from remote glacier lake monitoring stations to a central hub via radio frequency/ long-range communication, or satellite communication technology. Key properties such as lake water level, temperature, hydro pressure, and seismic activity are continuously recorded and transmitted from remote areas to a central monitoring station (CMS). The CMS examines incoming data using risk assessment algorithms (thresholds), which provide warnings and notifications as soon as potential GLOF issues are detected. The integration of this DEWS with local emergency response plans allows for more prompt and informed decisionmaking, which helps to mitigate GLOF-related tragedies. This comprehensive system intends to improve community resilience by delivering early warnings and facilitating proactive reaction measures.

Keywords: GLOF, EWS, Disaster, Pressure sensor, LoRa.

data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md


Document: 1 Introduction
Section: 1 Introduction
Type: text

A GLOF is defined as a rapid and catastrophic overflow due to the breakage of the moraine dam which dams a glacial lake. Normally, the moraine dams of a glacial lake comprise unconsolidated rocks and sediments, which are sensitive to climatic changes and inputs generated by seismic activity as well as anthropogenic factors [1]. However, GLOF disasters may lead to destruction in the lower areas. This results in severe destruction and loss [2], [3] Climate change is resulting in increased incidence and intensity of GLOFs. Hence, the current need to increase early monitoring and risk mitigation of GLOF. Effective early prediction of GLOF It will update you on dangers of GLOF in advance. This can be done with various methods like

- Remote Sensing: Besides the satellite image, there is other remote sensing-based data that could be used in monitoring the changes in the size of glacial lakes, water level, among other indicators of GLOF risk.
- Ground-based monitoring: Ground-based stations are also used in gathering data on several parameters, including water levels, turbidity, and many more.
  - •Numerical modeling: Numerical models can simulate how glacial lakes and moraine dams behave under different conditions.

- GLOF mitigation aims at reducing the chances of a GLOF happening or its impacts if one happens. Some of the possible strategies adopted include:
- Water level in the glacial lake: It can be drained with a controlled release or even by developing tunnels or spillways.
- The moraine dams may be reinforced by either adding more material within the dam or constructing support structures.
- •Relocation of communities and infrastructure outside hazard zones of GLOFs is considered the most effective measure of mitigation taken in the long term.
  - Early Warning Systems (EWS): EWS may give the communities an opportunity to evacuate before the occurrence of a GLOF event. Most EWS depend on the combination of several early detection means with a communication system that issues warnings to affected communities.

The work on GLOF early detection and mitigation has been ongoing, and the new methods and technologies are in the pipeline very continuously. The most important thrust in such work is on making more appropriate and reliable techniques for early detection. The other key areas of research include developing cost-effective sustainable mitigation. In the current focus, this research relates to the design of DEWS for GLOF with real-time measurement and communication of data, analysis, and action.


Document: 1 Introduction
Section: 2 Literature review
Type: text

GLOFs are one of the foremost natural hazards occurring in mountainous regions, especially in the Himalayas and Andes. This is following the breach of the glacial lake dam, releasing a massive amount of water and debris that travels downstream. GLOFs also trigger incredible damage to structures, properties, and human life. Of late, there has been more frequency and intensity along with climate and glacier retreat. This requires GLOF detection with adequate EWS in place to mitigate the risks associated with such floods.

There are several studies that have been conducted on GLOF detection and EWS. For example, Wang W (2022) et.al [4] designed an early warning system for the Cirenmaco glacial lake located in the central Himalayas. The system is more interested in monitoring and providing various features with timely warnings rather than specific flood features. Another study about the computational challenges and solutions involved in the development of GLOF early warning system by Kumar B (2022) et.al [5]. It believes that an early warning system is required to monitor vulnerable glacier

lakes and provide robust tools to the disaster managers to plan mitigation, thus saving human life and property. International Centre for Integrated Mountain Development (ICIMOD) has also made some studies on GLOF detection and EWS. In the report of Mitigation (2008) [6], ICIMOD speaks about EWS, monitoring, and GLOF mitigation measures. The GLOF warning systems were based on 'extended line of sight' (ELOS) VHF radio technology. The warning signal would be transmitted via ELOS ground wave

An early warning system on glacial lake outbursts to save lives and livelihoods of Nepal Himalaya communities was developed through a joint initiative of the Government of Nepal, UNDP in Nepal, and the Global Environment Facility [7]. The system encompasses hydro-met stations; GLOF sensors automated early warning sirens and a linked dynamic mass SMS alert system polygon. The system benefits more than 71,000 vulnerable people; both the local and the tourists visiting the Everest Region of Nepal. The system has the potential for replication and upscaling, with 21 critical lakes in Nepal and 25 in Tibet, China. Integration of climate actions in the routine planning and implementation holds the key for resilient and sustainable development. Among these, Kumar B (2020) et.al [5] proposed an ultrasonic sensor-based design architecture of a GLOF early warning system. The study majorly depicts the need to have an early warning system, especially one that detects GLOFs in real-time with timely warnings to the communities at risk.

The monitoring of GLOF and EWS is crucial for risk management associated with GLOF. This includes the deployment of remote sensing, ground-based sensors, and machine learning algorithms that incorporate data collection, processing, and analysis methods in the detection and prediction of GLOFs. These systems have been evaluated based on real-world data compared with existing GLOF detection and EWS. Besides this, new insight coupled with enhanced process understanding facilitated from reconstructing past GLOFs has contributed significantly to effective detection of GLOFs and EWS [8].


Document: 1 Introduction
Section: 3 Insights for CIIRC design > • Measuring Water Pressure:
Type: text

Measured at the installation point, the system measures the pressure of water within streams or rivers and in glacial lakes. Sudden changes in pressure may indicate a rising level of water-a warning sign for possible GLOF. A rise in hydro pressure could initiate subglacial conduits or tunnels, which would further weaken the glacier dam. By monitoring hydro pressure, the likelihood and triggering of a GLOF can be reflected and a prognosis drawn on the potential for dam failure.

3 Insights for CIIRC design
• Measuring Water Pressure:


Document: 1 Introduction
Section: 3 Insights for CIIRC design > Measuring Water Level:
Type: text

The system also monitors the fluctuations of the water level in the lake, which will trigger alarms with quick increases. Rapid rises in water level might be indicative of

sudden water inflow, perhaps due to a breach in a glacier dam or icefall collapse. Ongoing monitoring of water level conditions can hence provide advance warnings to communities downstream.

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3 Insights for CIIRC design
Measuring Water Level:


Document: 1 Introduction
Section: 3 Insights for CIIRC design > Measuring Temperature:
Type: text

Continuous monitoring of temperature offers valuable information relating melt rates with changes in lake, river, and stream dynamics. Changes in lake water temperatures may indicate changes in the amount of glacial meltwater input, which can be due to increased ice melting or changes in the environment of glacial regions. Monitoring this change identifies the actual stability of the glacier lake. Statistical anomalies in lake water temperature, among other monitoring parameters, can generate alarms and warnings. A quick and steady rise in temperature may be an initial sign of the changes in the glacial environment and may give an early prelude to a GLOF.

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3 Insights for CIIRC design
Measuring Temperature:


Document: 1 Introduction
Section: 3 Insights for CIIRC design > • Measuring Seismic activities:
Type: text

It can capture sudden movements or changes in the glacier structure that may be a precursor to a GLOF event, providing more detail about the evolving dynamics. Recording acceleration or changes in velocity can add valuable data in many ways. Abrupt changes in seismic activity near the glacier dam may indicate structural weaknesses or failure points. Continuous monitoring will be able to deliver an estimation of probable loss due to dam failure and subsequent water overflow. Seismic signals that occur with a GLOF can be studied to determine more about the dynamics of the events, including the time of occurrence and the magnitude. This information will be used in understanding the characteristics of the flood and increasing the ability to analyze future risks. Pressure sensors combined with seismic sensors will enable subglacial processes to be monitored. For instance, even the process of creating subglacial conduits or tunnels may affect pressure dynamics and cause seismic signals. Integration of pressure and seismic data could thus offer a more defined view of subglacial activity.

4 The DEWS Design flow

- The proposed PSA based DEWS has been presented in a block diagram in figure 1. The proposed DEWS, therefore, caters to a specific requirement such as glacial lake monitoring and assessing the risk of GLOFs. The system, thus, is provided with Pressure Sensing Apparatus (PSA) along with a communication system, well suited to provide real-time data, such as hydro pressure and water level measurements and temperature measurements. The system can also incorporate a seismic sensor for
- instance accelerometers for monitoring the seismic activity.

It has advanced communication interfaces such as RF/long range (LoRa)/ SATCOM modules (transmitter module at GLOF site and receiver module at central monitoring station) for early warning capabilities. Integrated data loggers of the pressure sensor (DCX-22) can be programmed to record and transmit the data to a central monitoring

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station at regular intervals (minimum interval of 1min). Real-time data transmission can be derived from satellite communication, with suitable uplink and downlink facilities or through any other form of wireless RF communication means thus ensuring real-time monitoring at the location. The moment remote-sensing-based data from the DEWS is available, a DAS comes into play as a fundamental part of the early warning process.

The DAS is under constant surveillance, checking the real-time data against set thresholds and patterns. Such thresholds are mostly set by topographical data and are carefully fine-tuned by the insights gained from historical and researched contexts. The data acquisition system instantly sends an alarm signal whenever deviations cross a set threshold. The risk is thus passed to the concerned authorities, usually present at site in the locality; in this case, the local community in-charge / National Disaster Management Authority, NDMA through the signal and hence causes an urgency call for action. Advanced communication technology hastens the alarm signal and disaster management becomes urgent with a current emphasis. With the NDMA being the custodian authority, it thus coordinates quick and prompt disaster response to manage or lessen the impending disasters. Such responses by the NDMA will include various emergency measures that involve the inclusion of immediate alerts to the communities at risk. Such alerts will include such actions as emergency evacuation messages to people living in the vicinity of the disaster, thus reducing possible risks and saving lives and livelihoods.

3 Insights for CIIRC design
• Measuring Seismic activities:


Document: 1 Introduction
Section: 3 Insights for CIIRC design > • Measuring Seismic activities:
Type: figure
Figure

Image /page/5/Figure/4 description: A block diagram illustrating a system for monitoring Glacial Lake Outburst Floods (GLOF). The diagram is divided into two main parts. The left part, labeled "Glacial Lake at a Remote Place," shows a process flow starting from a "GLOF Site" to a "Pressure Sensor," then to "PSA," then to "Glacial lake parameters," and finally to an "RF / SATCOM / LoRa Transmitter" which sends a signal via an antenna. The right part, labeled "Central Monitoring Station," shows a signal being received by an antenna connected to an "RF / SATCOM / LoRa Receiver." The data then goes to a "Data Acquisition System (DAS)." A decision block checks for the "Threshold for GLOF." If the condition is not met ("No"), the process loops back to the DAS. If the condition is met ("YES"), an "Alarm to Local community / NDMA" is issued.


Document: 1 Introduction
Section: 3 Insights for CIIRC design > • Measuring Seismic activities:
Type: text

F


Document: 1 Introduction
Section: 5 Proposed Pressure Sensing Apparatus (PSA)
Type: figure
Figure: Figure 2 Shows the graphic layout of the PSA. A high strength hose 10m long and 26mm in diameter is used, where accurate measurements of the pressure are ensured by the pressure sensor contained in this hose. This sensor is fixed on one end of an iron rod larger than the internal diameter of the hose at about 2cm in length. The rod is inserted in the hose using silicone sealant, but the lower end is held inside with hose clamps. It served the purpose by giving a waterproof seal and provides weight to stabilize the sensor's position. At the top of the hose, a T-junction is fitted so that a logger cable can be connected to connect up a pressure sensor to the data logger. The second leg of this T-junction feeds out to a 2-liter rubber expansion bladder. This bladder is normally half full; this way, volume changes in the antifreeze solution, such as those due to solar heating, build only enough pressure in the assembly that it can easily be sealed. The hose is filled with an appropriate quantity of antifreeze and water; the illustration here is 50:50 mix of ethylene glycol with water. It serves as the antifreeze solution. In establishing the installation of the PSA, a pole with a length of 5 meters is installed in the central region/ shallow side/ shallow bank of the stream/river. The length of the pole depends on the demand. The guides attached to the Teflon pulley hold the PSA on the pole. The pulley system makes sure that water level fluctuations do not pull the pressure sensor off of the river or streambed [9]. In case the PSA is deployed on an ice floor of a frozen pond/lake, this pulley arrangement allows the pressure sensor to dip as the ice melts, thereby ensuring that it remains in contact with the underlying surface of the pond or lake where the meltwater collects. The installation process involves using drilling equipment with extensions to a depth of 3 meters in the riverbed or streambed or frozen pond/lake.

Figure 2 Shows the graphic layout of the PSA. A high strength hose 10m long and 26mm in diameter is used, where accurate measurements of the pressure are ensured by the pressure sensor contained in this hose. This sensor is fixed on one end of an iron rod larger than the internal diameter of the hose at about 2cm in length. The rod is inserted in the hose using silicone sealant, but the lower end is held inside with hose clamps. It served the purpose by giving a waterproof seal and provides weight to stabilize the sensor's position. At the top of the hose, a T-junction is fitted so that a logger cable can be connected to connect up a pressure sensor to the data logger. The second leg of this T-junction feeds out to a 2-liter rubber expansion bladder. This bladder is normally half full; this way, volume changes in the antifreeze solution, such as those due to solar heating, build only enough pressure in the assembly that it can easily be sealed. The hose is filled with an appropriate quantity of antifreeze and water; the illustration here is 50:50 mix of ethylene glycol with water. It serves as the antifreeze solution. In establishing the installation of the PSA, a pole with a length of 5 meters is installed in the central region/ shallow side/ shallow bank of the stream/river. The length of the pole depends on the demand. The guides attached to the Teflon pulley hold the PSA on the pole. The pulley system makes sure that water level fluctuations do not pull the pressure sensor off of the river or streambed [9]. In case the PSA is deployed on an ice floor of a frozen pond/lake, this pulley arrangement allows the pressure sensor to dip as the ice melts, thereby ensuring that it remains in contact with the underlying surface of the pond or lake where the meltwater collects. The installation process involves using drilling equipment with extensions to a depth of 3 meters in the riverbed or streambed or frozen pond/lake.


Document: 1 Introduction
Section: 5 Proposed Pressure Sensing Apparatus (PSA)
Type: figure
Figure

Image /page/6/Picture/4 description: A diagram illustrates a monitoring system set up in a glacial lake. The main part of the diagram shows a cross-section of a body of water labeled "Glacial Lake," with a device submerged in it. This device is connected by a hose to equipment on the shore. Two inset diagrams provide detailed views of the components. A red arrow points from the submerged device to a close-up diagram showing its internal structure. This structure is a long tube containing a "Metal sinker" at the bottom, a "Pressure sensor" above it, a "Cable," and "Antifreeze liquid," with a "Hose" connected at the top. Another red arrow points from the equipment on the shore to a second close-up diagram. This diagram shows a green, spherical "Bladder" connected via a "Hose" and "Fittings" to a "Cable to logger."


Document: 1 Introduction
Section: 5 Proposed Pressure Sensing Apparatus (PSA)
Type: text

Fig. 2. Design flow


Document: 1 Introduction
Section: 5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device
Type: text

The DEWS can usually contain several sensors and equipment to measure water level, temperature, pressure, or a single pressure sensor like DCX-22 (which can work up to -10-degree celcius without the PSA setup / with PSA setup can work up to -80 celcius). The KELLER Druckmesstechnik AG, Switzerland sensor DCX-22 of figure 3 has a logger for data and can measure the water level, hydro pressure, and temperature. The pressure sensor used in the PSA is DCX-22 and is flexible to measure the water level or pressure of water and temperature. The sensor is also able to give accurate readings for submerged applications. With the data logger, which is equipped with a long-lasting battery, measurements can be recorded for long periods of time, from minutes to hourly intervals. Also, the pressure sensors are able to be calibrated by the users using the calibration program provided by the vendor. Sensors must be calibrated in an environment exactly the same as that in which they will be used. Sensors are often calibrated upright on a level surface. Sensor recalibration may be needed in several cases, such as when the sensor is serving under new or changed maintenance conditions, its measuring setup is altered, or its station has been in service for many years.

The pressure sensor, in combination with the data logger, can be programmed to acquire data on hydro pressure and temperatures for set time intervals. The result ensures that the collection of measurements during the monitoring period will be accurate. Nevertheless, it could be such that there will be cases wherein data gaps may have occurred through the exposure of the sensor to either freezing or low water levels, mostly. The densities of the surrounding environment should be considered when determining the height of a water level using the measurements of hydrostatic pressure acquired with the help of the pressure sensing device. Equation 1 supports the conversion of pressure readings in N/m2 to the corresponding height of the water column in meters. Another important consideration in the calculations is that the length of an iron rod needs to be added to the depth/water level for appropriate estimates.

$$d = \frac{P}{(0*9)} \tag{1}$$

where P = hydrostatic pressure (Nm-2),  $\rho = water$  density (kgm-3), g = gravitational acceleration (ms-2), d = height of water column (m).


Document: 1 Introduction
Section: 5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device
Type: figure
Figure

Image /page/7/Picture/7 description: A studio photograph of an industrial sensor on a white background. The device consists of a long, cylindrical stainless steel probe connected to a long, coiled black cable. The other end of the cable terminates in a multi-part stainless steel connector. The tip of the probe is a black cap with small holes, and the connector also has a visible sensor element at its end.


Document: 1 Introduction
Section: 5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device
Type: text

Fig. 3. DCX-22 KELLER Pressure Transducer


Document: 1 Introduction
Section: 7 Installed PSA at field
Type: text

The configuration in figures 4 and 5 here constitutes the working scheme of the PSA: the RF transmitter is located at a remote position, while the RF receiver mounted with a Data Acquisition System (DAS) is located at the central monitoring station. This design, primarily aimed at the purpose of observing depth and water levels in lakes in Antarctica, promises much towards the detection of GLOFs. In fact, by serving as a DEWS, it can be fundamentally important in the premature observation and alerting authorities about possible GLOF catastrophes. As a result of unfavorable climatic conditions and inaccessibility of regular transportation for frequent visits, the system has been engineered to possess wireless communication capabilities. This allows real-time data transmission from the installation site to a central monitoring station about 2 kilometers off the lake site. Long Range (LoRa) transmitter and receiver modules are chosen in the wireless communications technology that will be made use of for this system since this will allow the transcription of critical lake parameters, like hydro pressure, temperature, and water level.

The system comprises reliable backup mechanisms in terms of power to continuously and uninterruptedly transmit and receive data. It employs the use of solar panels and insulation for the proper protection of the parts constituting the transmitter and receiver from probable disruptions through power and ensures a smooth, steady flow of data. The pressure sensor is programmed to become operational to serially measure the lake parameters at regular intervals. The acquired data is then sent to the central monitoring station, as exemplified in figure 5. This systematic approach, of course, allows comprehensive data to be collected but also ensures timely updates at the monitoring station-thus making the whole system of monitoring effective in the challenging polar environment.


Document: 1 Introduction
Section: 7 Installed PSA at field
Type: figure
Figure

Image /page/8/Picture/5 description: A scientific instrument is set up on the edge of a body of water in a snowy, icy landscape. The instrument consists of a metal pole with a grey box labeled "CRANE" in red letters at the top, and a reel with tubing attached to the side. The pole is mounted on a wooden plank that extends from a rocky bank over the partially frozen water, with the bottom of the pole submerged. The background is a vast expanse of snow and ice under a blue sky.


Document: 1 Introduction
Section: 7 Installed PSA at field
Type: text

Fig. 4. Working model of PSA setup installed in a lake.


Document: 1 Introduction
Section: 7 Installed PSA at field
Type: figure
Figure

Image /page/9/Figure/2 description: A composite image, labeled Fig. 5, containing five panels labeled (a) through (e), illustrating a data acquisition system and its output.


Document: 1 Introduction
Section: 7 Installed PSA at field
Type: text

Panel (a) shows a small solar panel and an electronic device in a blue case placed on rocks outdoors.

Panel (b) is a close-up of the electronics inside a box, including a yellow battery pack labeled "LEMON 14.8V", a blue circuit board, and various wires.

Panel (c) depicts a person sitting at a desk, working on a laptop with other computer equipment nearby.

Panel (d) is a line graph plotting Temperature (°C) versus Time. The y-axis ranges from -6 to 12. The x-axis shows time over a 24-hour period. There are two lines: a blue line for "Water Temperature" and an orange line for "Surface Temperature". The water temperature fluctuates more widely, peaking around 11°C, while the surface temperature remains lower, peaking around 2°C.

Panel (e) is a screenshot of a spreadsheet with data columns for Time, Timestamp, Date, CH1 Pressure(mbar), and CH2 Temperature(Â°C). The data shown is for 16-Jan-23, with timestamps starting at 12:44:29. Sample data includes a pressure of 984.68 mbar and a temperature of 5.6°C.

The caption below reads: "Fig. 5. (a) & (b) Transmitter module with solar panel and power backup (c) Sample..." with the rest of the text cut off.

**Fig. 5.** (a) & (b) Transmitter module with solar panel and power backup (c) Sample data measured at 15min interval by PSA (d) Central monitoring station with receiver and (e) Sample of real time data received from PSA at 1 minute interval using LoRa receiver


Document: 1 Introduction
Section: Features of proposed DEWS
Type: text

- The proposed Pressure Sensing Assembly can come out as an important monitoring device for Glacial Lake Outburst Floods and therefore can help in the prevention or reduction of its impact. With an interfacing of an appropriate wireless communication
- system with the PSA, it is likely to provide real-time data to the NDMA via early warnings through PSAs in the following ways:
- 1. An Early Warning System: Place the PSA strategically around/at the glacial lakes. It will constantly observe the change in water pressure and lake levels. If the water pressure increases rapidly, along with an increase in the water level, that might depict a GLOF event. The sooner an event can be observed, the better it can alert downstream
- 2. Real-time Data: The PSA can also forward in real time data concerning pressure levels and the temperature of the water. These data then forwarded to a central monitoring station for close scrutiny by experts to determine emerging trends that could characterise an impending GLOF.
- 3. Trend analysis: So, with this data for very long periods, the scientists and researchers get to understand how this glacial lake acts historically, thus they will be in a better position to identify some trends and patterns which may have been happening before these GLOFs. It means better predictions are made, and pro-active mitigations can be developed.
- 400 4. Risk Assessment: By these continuous updates of the lake conditions, PSA facilitates risk assessment towards GLOF. Information collected in this context may help policymakers decide about evacuations, infrastructure development, and so on.
- 5. Remote Access: The PSAs that are equipped with remote data transmission, can prove very useful in remote and inaccessible glacial regions. This will enable experts to monitor conditions and collect data even from challenging environments.

Regular maintenance and calibration of the PSA is essential to ensure that accurate data would be collected. Proper maintenance also prevents one from false alarms hence ensuring that one's PSA would still be reliable for GLOF monitoring. To minimize data loss from a low battery, a solar panel can be interfaced to charge the system. In a nutshell, PSA has become an essential tool in early warnings, assessments, and monitoring of glacial lakes, which contribute to preventing and mitigating the impacts of GLOFs, finally serving to safeguard communities downstream from ice melts that trigger outburst floods.

The development and deployment of a dedicated Disasters Early Warning System for Glacial Lake Outburst Floods are hence among the very important advances into mitigating impact involvement with this kind of catastrophic happening. Integration of satellite communication technology makes it possible to monitor remote glacial lake conditions in real time, thus enabling the early detection of potential GLOF risks. Combining data from sensors measuring the levels of water, temperature, and hydro-pressure activity, the system uses complex algorithms to riskassess and generate accurate alerts. The addition of a seismic sensor in this system will enable monitoring of seismic activity. Successful integration of this DEWS with local emergency response plans helps communities get warnings in time, or even permit an efficient evacuation and response strategy. Because climate change is increasingly influencing glacial dynamics, this system would be proactive in protecting the most vulnerable populations against the rising threat of GLOFs, thus supporting sustainable disaster risk reduction efforts. In general, DEWS the CIIRC structure is very robust and likely to be with us for a long time. This assurance originates from its launching and rigid testing under the extreme conditions of Antarctica's polar region for nearly three months.


Document: 1 Introduction
Section: 438 Acknowledgment
Type: text

- The authors gratefully acknowledge the support rendered by Centre for Incubation,
- 440 Innovation, Research and Consultancy (CIIRC), Jyothy Institute of Technology,
- 441 Bengaluru and Sri Sringeri Sharada Peetham.


Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information
Type: text

Abstract: Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

**Keywords:** disaster prediction knowledge graph; spatio-temporal; disaster dynamic prediction; multi-source data fusion; forest fire risk prediction; geological landslide risk prediction

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


Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information
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Figure

Image /page/0/Picture/14 description: A graphic on a white background features a yellow circular icon with a white checkmark inside. To the right of the icon, the text 'check for updates' is displayed in a dark sans-serif font. The words 'check for' are on the top line, and the word 'updates' is on the bottom line in a bolded font.


Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information
Type: text

Citation: Ge, X.; Yang, Y.; Chen, J.; Li, W.; Huang, Z.; Zhang, W.; Peng, L. Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information. *Remote Sens.* 2022, 14, 1214. https://doi.org/10.3390/rs14051214

Academic Editors: Hideomi Gokon, Yudai Honma and Shunichi Koshimura

Received: 31 January 2022 Accepted: 26 February 2022 Published: 1 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information
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Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information
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Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).


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

Disaster is a general term for events that can have a destructive impact on humans and the environments on which they depend. Although natural disasters are inevitable, it must take efforts to greatly reduce losses. However, due to the wide distribution of natural disasters, time, location, and scale of natural disasters have great uncertainty. It greatly increases the difficulty to resist natural disasters. Therefore, the critical issue is the prediction of disasters with multi-source data of disaster scenarios for formulating emergency plans.

Remote sensing technology can obtain a large amount of strongly dynamic information in large observation ranges with high speed in real-time [1]. It can identify the fine features of ground objects, and extract information such as buildings, roads, and cultivated land by combining remote sensing technology and artificial intelligence technology. It plays an important role in the fields of disaster monitoring, early warning, and emergency decision-making. The critical problem is the fusion of different data types, different data structures, and different types of expert knowledge when the remote sensing information extraction for the multi-scale, multi-temporal, multi-type, multi-precision remote sensing

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extraction information, other geographic information, and other spatio-temporal data in disaster scenes. There are studies for addressing the problem of spatio-temporal disaster information fusion, including knowledge representation of spatio-temporal disaster data, attribute analysis based on data spatio-temporal relations, and spatio-temporal dynamic geographic information of and expert knowledge.

In the real-world disaster scene, there exists large-scale multi-source heterogeneous data. There are complex associations between the data, as well as between the data and the disaster knowledge. It can effectively represent and model the data as well as the in-between relations using a knowledge graph. This is an artificial intelligence technology proposed by Google in 2012. Knowledge graph is a data modeling method that represents knowledge as concept, entity, and semantic relations with each other in the form of a graph [2]. Knowledge graphs can connect multi-source spatio-temporal disaster data and expert knowledge by a graph in order to support the analysis of the multi-source heterogeneous data under dynamic and static situations in natural disaster scenarios.

The spatio-temporal knowledge graph is a knowledge graph that can realize the semantic association of spatio-temporal data, which is the basis of disaster prediction. Sun et al. proposed a spatio-temporal knowledge graph ontology model which can manage and query spatio-temporal data in specific fields by adding spatio-temporal attribute information to entities [3]. Geographic information is a type of spatio-temporal data with spatial and temporal characteristics. Therefore, the construction technology of a geographic knowledge graph is very meaningful for this study. Zhang et al. proposed a method for constructing a geographic knowledge graph that takes the spatio-temporal characteristics into account. They studied the formal representation of geographic knowledge and laid a foundation for the acquisition, fusion, reasoning, and application of the spatio-temporal data [4]. Based on ontology, Liu et al. proposed a fast retrieval method that takes the semantic knowledge of geospatial data into account for reducing users' dependence on data storage methods and database grammar rules [5]. Jiang et al. proposed the construction process of a geographic knowledge graph, which helps to realize the knowledge of geographic information by reviewing the geographic knowledge graph [6].


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

constructing a geographic knowledge graph that takes the spatio - temporal characteristics into account . They studied the formal representation of geographic knowledge and laid a foundation for the acquisition , fusion , reasoning , and application of the spatio - temporal data [ 4 ] . Based on ontology , Liu et al . proposed a fast retrieval method that takes the semantic knowledge of geospatial data into account for reducing users ' dependence on data storage methods and database grammar rules [ 5 ] . Jiang et al . proposed the construction process of a geographic knowledge graph , which helps to realize the knowledge of geographic information by reviewing the geographic knowledge graph [ 6 ] .

At present, there exists research work regarding the construction of ontology and knowledge graph for specific disasters. Qiu et al. constructed model ontology and data ontology for flood management based on the four phases of disaster management [7]. Xu et al. constructed a conceptual model for earthquake disasters based on geo-ontology and relevant rules of earthquake emergency decision-making [8]. Scheuer et al. constructed flood risk assessment ontology by integrating SWEET ontology and MONITOR ontology [9]. Tao et al. summarized the knowledge graph construction process and key methods for the integrated comprehensive disaster reduction, and took the Jiuzhaigou earthquake as an example to demonstrate the knowledge graph construction process and construction results for Jiuzhaigou earthquake disaster reduction [10]. However, the above studies only focused on the knowledge representation and ontology construction for a single disaster type when building a knowledge model without considering the unified knowledge graph for different disaster types.

Furthermore, the existing knowledge graph models usually do not focus on strategies and rules based on common semantics so that it is hard to provide specific disaster-handling strategies in disaster emergency scenarios for supporting decision-making. The disaster prediction knowledge graph developed by Qiu et al. [7] visualizes the disaster situation. It can more autonomously determine the environmental models and disaster-related data used in different disaster phases using the semantic constraints defined in the ontology [11]. Li et al. constructed a knowledge graph for flow disasters with the personalized visualization of different scenes for multilevel users [12]. Xie et al. combined the complex semantic relationship between earthquake prevention and control entities by extracting various earthquake disaster information, and established a semantic network by constructing a knowledge graph of earthquake disaster prevention and mitigation as the core [13]. Du et al. proposed a top-down and bottom-up method for constructing a

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natural disaster emergency knowledge graph [14]. They realized the transformation from multi-source data to interconnected knowledge by taking the flood disaster emergency knowledge graph as an example for experimental verification. At present, the most related research on the disaster knowledge graph respectively works on the perspectives of disaster ontology, disaster knowledge, and disaster events, particularly paying attention to construction methods. In applications, there are only domain knowledge-driven analyses. This rarely integrates dynamic disaster data and model methods deeply for the analysis based on the fused multi-feature fusion. It leads to the problem that the emergency plan cannot be flexibly adapted to the actual situations.


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

knowledge graph [ 14 ] . They realized the transformation from multi - source data to interconnected knowledge by taking the flood disaster emergency knowledge graph as an example for experimental verification . At present , the most related research on the disaster knowledge graph respectively works on the perspectives of disaster ontology , disaster knowledge , and disaster events , particularly paying attention to construction methods . In applications , there are only domain knowledge - driven analyses . This rarely integrates dynamic disaster data and model methods deeply for the analysis based on the fused multi - feature fusion . It leads to the problem that the emergency plan cannot be flexibly adapted to the actual situations .

Affected by the attribute characteristics of surface elements, the development of disasters will show certain patterns when the disasters spatially migrate and change over time. This study selects forest fires and geological landslides as typical research objects. This paper proposes the Disaster Prediction Knowledge Graph (DPKG) that facilitates multi-source spatio-temporal data management and knowledge association. The proposed method supports semantic association of spatio-temporal data, efficient query, and quantitative analysis in order to implement information analysis with multiple types of disasters. The proposed method can be used for intelligent deduction of disaster situation and intelligent identification of disaster risk. This paper demonstrates experiments for verifying the benefits of the proposed method with respect to forest fire prediction and geological landslide risk prediction.

The main contributions of this paper are as follows: (1) This paper proposes a disaster prediction method which realizes dynamic data-driven disaster prediction based on the DPKG. (2) This paper proposes a fusion method for the dynamically changing spatio-temporal data and knowledge model. The proposed method is beneficial to improve the ability of natural disaster monitoring, early warning, and emergency response. It can provide a quantitative reference for disaster prediction and play a practical role to a certain extent.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture
Type: text

This section describes the theoretical way for mapping disaster prediction scenarios to knowledge graphs, mainly including the hierarchical semantic model of disaster prediction, and the mapping relationship between disaster prediction scenarios and DPKG architecture.

2. Materials and Methods
2.1. DPKG Architecture


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios
Type: text

The occurrence of disasters is related to a variety of factors. It facilities disaster prediction and decision-making by the analysis of disaster-inducing factors and their interrelationships.

The affect factors of occurrence of natural disasters are composed of common factors and characteristic factors. The common factors are closely related to different types of disasters. The characteristic factors are closely related to the occurrence of a certain natural disaster and weakly related to other types of natural disasters. This study establishes a hierarchical semantic model of disaster prediction considering the affect factors, which provided a theoretical basis for the construction of a conceptual layer for forest fire risk prediction and geological landslide risk prediction, as shown in Figure 1. Aiming at the prediction of forest fires and geological landslides, this paper extracts the common factors and characteristic factors of disaster occurrence by considering remote sensing information (vegetation, farmland, roads, buildings, and water bodies), terrain, meteorology, and human factors closely related to the occurrence of disasters.

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios
Type: figure
Figure

Image /page/3/Figure/1 description: A flowchart illustrating the factors used for Forest Fire Risk Prediction and Geological Landslide Risk Prediction. The chart is divided into three color-coded sections. On the left, in a pink section, are "Common factors in disasters," which include Terrain (Slope, Aspect, Height), Meteorological (Wind speed, Wind direction, Precipitation, Temperature, Humidity), and Ground cover (Arable land, Woodland, Water, ......). These common factors feed into a central box labeled "Disaster Occurrence Characteristics." From this central box, the chart branches into two specific disaster types. The top right, in a blue section, details factors for "Forest Fire Risk Prediction," specifically the "Combustibles factor," which includes Vegetation types, Vegetation Coverage, Species, and Tree age. The bottom right, in a green section, details factors for "Geological Landslide Risk Prediction," specifically "Geological factors," which include Stratigraphic lithology, Fault structure, Surface roughness, Surface relief, Plane curvature, and Section curvature. Brackets indicate that the common factors combined with the combustibles factor lead to Forest Fire Risk Prediction, and the common factors combined with geological factors lead to Geological Landslide Risk Prediction.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios
Type: figure
Figure: Figure 1. A hierarchical semantic model for disaster prediction scenarios regarding forest fires and geological landslides.

**Figure 1.** A hierarchical semantic model for disaster prediction scenarios regarding forest fires and geological landslides.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture
Type: text

For modeling disaster prediction using a knowledge graph, it is necessary to represent the disaster prediction scenario as the hierarchical semantic model. It maps the data with spatio-temporal characteristics in the disaster prediction scenario into the DPKG according to the structure of the semantic model. The disaster analysis model can be transformed into knowledge. The DPKG also needs to integrate the transformed disaster analysis model and expert knowledge for disaster prediction.

In addition, the architecture design of the DPKG for disaster prediction needs to consider the fusion of multi-source heterogeneous spatio-temporal data including remote sensing extraction information, terrain, meteorology, vegetation, and human factors, as well as the fusion of spatio-temporal data with disaster analysis models and expert knowledge for linkage analysis. This study designs an architecture of DPKG for disaster prediction, as shown in Figure 2, in order to support the modeling of disaster prediction and the collaborative analysis with fused data.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture
Type: figure
Figure

Image /page/3/Figure/6 description: A flowchart diagram illustrating the 'DPKG architecture'. The architecture is divided into several components within nested boxes. The main section is split into two parts: 'Set of rules (TBox)' and 'Set of facts (ATox)'. The 'Set of rules (TBox)' contains a 'Conceptual layer' (with 'Space ontology', 'Time ontology', and 'Common semantic ontology of disaster prediction') and 'Inference rules' (with 'First-order logical reasoning rules', 'Production inference rules', and 'Spatio-temporal semantic reasoning rules'). The 'Set of facts (ATox)' shows a flow where 'Unstructured data', 'Semi-structured data', 'Structured data', and 'Disaster prediction reasoning criterion' feed into a 'Knowledge extraction' process, which results in a 'Set of facts (ABox)'. An arrow points from this main section to two modules at the bottom: 'Disaster fusion data spatio-temporal semantic query module' and 'Disaster prediction rule reasoning module'.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture
Type: figure
Figure: Figure 2. DPKG architecture.

Figure 2. DPKG architecture.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture
Type: text

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The DPKG for disaster prediction consists of two parts, namely the rule set (TBox) and the fact set (ABox). The TBox consists of the conceptual layer and the inference rules. The conceptual layer is the common semantic basis for describing the hierarchical relationship of various factors in disaster prediction scenarios. The inference rules are the common logic basis for multi-source spatio-temporal data that supports semantic reasoning. The ABox constitutes the instance layer of the knowledge graph, which contains the time, space, status, and other attribute information of various types of ground objects related to disaster emergency extracted from multi-source heterogeneous data. The ABox and the TBox constitute the reasoning basis of the DPKG. Through the rapid retrieval of spatio-temporal data in disaster areas, multi-source spatio-temporal data analysis can be realized. The architecture diagram will be described in detail later.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph
Type: text

This section introduces the knowledge representation language, the design of the conceptual layer and instance layer, and the knowledge representation of the disaster prediction model.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.1. Knowledge Representation Language
Type: text

Knowledge graph representation refers to the use of computer symbols to scientifically mark the objectively existing knowledge for facilitating the semantic reasoning [15].

OWL (Web Ontology Language) is a semantic description standard including three sub-languages: OWL-Lite, OWL-DL, and OWL-Full. At present, OWL is the most standardized, rigorous, and expressive language for a knowledge graph. The commonly used knowledge representation form is RDF (Resource description framework) triples. Each piece of knowledge can be represented as the following triple format: (subject, predicate, object), such as (vegetation, yes, combustible). This study chooses OWL as the knowledge representation language.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer
Type: text

The conceptual layer of the DPKG is a logical structure for multi-source spatiotemporal data. It contains semantic concepts and their interrelationships. Based on the semantic associations between different concepts, such as the subordinate relationship, attribute subject-object relationship, a tree-like hierarchical concept triple interconnection network is constructed for ensuring the consistence of the intrinsic semantic concepts of multi-source spatio-temporal data. The conceptual layer of disaster prediction DPKG is composed of time ontology, space ontology, and disaster prediction common semantic ontology.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction
Type: text

The dynamic prediction of forest fires and geological landslide disasters needs the semantic reasoning and collaborative computing of multi-source spatio-temporal data.

For this goal, this paper proposes a tree-like taxonomy of multi-source concepts of geographic entity associated with multi-source spatio-temporal data. According to the taxonomy, concepts of geographic entity are divided into five fields. This paper defines the domain attribute predicates related to the concepts of each geographic entity.

The concepts of geographic entity constitute a common semantic ontology for the prediction of forest fire and geological landslide disaster.

The storage model of common semantic ontology is hierarchical and expandable. It builds the foundation for the predicate logic reasoning based on the common hierarchical semantic relationship of disaster prediction, as shown in Figure 3.

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction
Type: figure
Figure

Image /page/5/Figure/1 description: A black and white flowchart titled 'Conceptual Framework of Disaster Prediction KG'. The main concept branches into three categories: 'Time object', 'Space object', and 'Role object'. 'Time object' further divides into 'Effective time' and 'Time granularity'. 'Space object' divides into 'Geographic description' and 'Features'. 'Role object' divides into 'Mechanism' and 'Post'. The 'Features' category from 'Space object' is connected to a lower level of five boxes: 'Ground cover', 'Meteorological', 'Topography', 'Geology and lithology', and 'Historical disaster'.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction
Type: figure
Figure: Figure 3. Common semantic ontology of disaster prediction.

Figure 3. Common semantic ontology of disaster prediction.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology
Type: text

Time ontology provides a specification of unified time semantic representation in order to ensure that the time information of entities is comparable and computable. This paper leverages the Semantic Web Rule Language (SWRL) time ontology proposed by Stanford University for representing the common time concepts of the DPKG [16]. The logical structure of the SWRL time ontology is shown in Figure 4.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology
Type: figure
Figure

Image /page/5/Figure/5 description: A diagram illustrating a data model or ontology for time-related concepts. It shows several colored rectangular boxes connected by labeled arrows. A pink box labeled "ExtendProposition" connects to a central blue box "ValidTime" via two arrows: a solid one labeled "hasValidTime" and a dotted one labeled "hasPredictTime". The "ValidTime" box connects to a yellow box "Granularity" with an arrow labeled "hasGranularity". Below "ValidTime", a light green box "ValidInstant" and a yellow box "ValidPeriod" are shown as subclasses, indicated by arrows labeled "SubClassOf" pointing to "ValidTime". At the bottom, a large red box is labeled "xsd::DateTime". "ValidInstant" has an arrow labeled "hasTime" pointing to "xsd::DateTime". "ValidPeriod" has two arrows pointing to "xsd::DateTime", one labeled "hasStartTime" and the other "hasFinishTime".


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology
Type: figure
Figure: Figure 4. SWRL temporal ontology.

Figure 4. SWRL temporal ontology.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Space Ontology
Type: text

The expression of spatial ontology applies A Geographic Query Language for RDF Data (GeoSPARQL) [17] that is a geographic semantic query specification proposed by OGC (Open Geospatial Consortium) [18]. The geographic semantic query mainly includes the following three common contents.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary
Type: text

It represents all feature entities as subclasses of Spatial Objects. All geometric objects can be subdivided into points, linestrings, and polygons. Geometric objects are represented using features and geometric objects (Geometry) and their defined relationships, as shown in Figure 5.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary
Type: figure
Figure

Image /page/5/Figure/11 description: A class diagram illustrates the relationships between different spatial object types, prefixed with 'ogc:'. At the top is the superclass 'ogc:SpatialObject'. Two classes, 'ogc:Feature' and 'ogc:Geometry', inherit from 'ogc:SpatialObject'. There is a directed association from 'ogc:Feature' to 'ogc:Geometry' labeled 'ogc:hasGeometry'. Three other classes, 'ogc:Point', 'ogc:LineString', and 'ogc:Polygon', all have arrows pointing to the 'ogc:Geometry' class. The 'ogc:Geometry' class is detailed in a larger box with two sections: 'Metadata' and 'Serialization'. Under 'Metadata', the properties are listed as: 'ogc:dimension : xsd:int', 'ogc:coordinateDimension : xsd:int', 'ogc:spatialDimension : xsd:int', 'ogc:isEmpty : xsd:boolean', 'ogc:isSimple : xsd:boolean', and 'ogc:is3D : xsd:boolean'. Under 'Serialization', the properties are 'ogc:asWKT' and 'ogc:WKTLiteral'.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary
Type: figure
Figure: Figure 5. GeoSPARQL part of the ontology table.

Figure 5. GeoSPARQL part of the ontology table.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary
Type: text

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Geometry extension expression
Type: text

The GeoSPARQL specification follows the OGC standard for the expression format of point, line, and surface. For example, the coordinate information of a surface data will be recorded in the attribute of *ogc:asWKT*, where *WKT* stands for Well Known Text, which is the ASCII code representation method of spatial objects.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Topology extension query
Type: text

GeoSPARQL specifies eight query relationships for all spatial objects, as shown in Table 1. In addition, it provides many commonly used distance query functions (*geof:distance*), buffer query functions (*geof:buffer*), and convex Package constructor (*geof:convexHull*), to enhance spatial query.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Topology extension query
Type: table
Table: Table 1. GeoSPARQL spatial topological relationship.

| geof:sfEquals  | geof:sfDisjoint | geof:sfIntersects | geof:sfTouches  |
|----------------|-----------------|-------------------|-----------------|
| geof:sfCrosses | geof:sfWithin   | geof:sfContains   | geof:sfOverlaps |

geof:sfEquals geof:sfDisjoint geof:sfIntersects geof:sfTouches


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > Construction of First-Order Logical Inference rules
Type: text

First-order logic (FOL) is a formal system used in mathematics, philosophy, linguistics, and computer science. Specifically, hierarchical relationships and object attribute relationships can be described as first-order predicate logic, as shown in Figure 6. The common ontology, time ontology, and space ontology of disaster prediction are formally modeled with OWL language. The hierarchical relationship and attribute relationship implies a series of first-order logical reasoning.

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > Construction of First-Order Logical Inference rules
Type: figure
Figure: Figure 6. Hierarchical relationship and object attribute relationship.

Figure 6. Hierarchical relationship and object attribute relationship.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 2. Construction of Production Inference rule
Type: text

Due to the limitation of the knowledge representation of first-order logic, this paper applies SWRL (Semantic Web Rule Language). It extends the set of OWL axioms to include Horn-like rules. In this study, SWRL is used to further enhance the expression of rules for DPKG. A rule has the form, where both antecedent and subsequent are conjunctions of atoms written. Using this syntax, a rule asserting that the combination of the tree species' properties and the tree species' forest fire risk-level properties implies the tree's forest fire risk-level properties, as shown in Figure 7.

```
\langle species(?x,?y) \land forestFireRiskLevel(?y,?z) \Rightarrow forestFireRiskLevel(?x,?z) \rangle
```


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 2. Construction of Production Inference rule
Type: figure
Figure: Figure 7. SWRL inference rule example.

**Figure 7.** SWRL inference rule example.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule
Type: text

Although SWRL can support basic quantitative calculations, it cannot support quantitative analysis of space and time including spatio-temporal semantics. This study leverages the construction method of production inference rules to build the spatio-temporal semantic inference rules. The spatio-temporal semantic inference rule *RuleObject* is a set of rules for the automatic execution of reasoning programs by the DPKG. Each rule is composed

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of an event object TriggerObject and an action object ActionObject, which are expressed as RuleObject = (Tr, Ac), where Tr represents the event contained in the RuleObject, Ac represents the action object ActionObject contained in the RuleObject, and R is the reasoning result. The event TriggerObject in this article is defined as a triple that is represented as TriggerObject = (O, T, S), where O represents the geographic entity set contained in the event object, T and S respectively represent the intersection of the geographic entity set in the time dimension and the space dimension. A spatio-temporal co-occurrence scene with a set of geographic entities can be described as an event object that is a definition of the applicable conditions of an inference rule.

The event (or action) object concept is further subdivided into independent events (or actions) and event (or action) combinations, which together form the concept of knowledge inference rules. The logical structure is shown in Figure 8.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule
Type: figure
Figure

Image /page/7/Figure/3 description: A hierarchical diagram illustrating the structure of a 'RuleObject'. The 'RuleObject' is at the top and branches into two main components: 'TriggerObject' on the left and 'ActionObject' on the right. The 'TriggerObject' branch is further subdivided. It points to 'IndependentTrigger' and 'TriggerCombination'. 'IndependentTrigger' then points to 'NormalTrigger' and 'AbnormalTrigger'. 'TriggerCombination' points to 'AndTrigger Combination' and 'OrTrigger Combination'. The 'ActionObject' branch is similarly subdivided. It points to 'IndependentAction' and 'ActionCombination'. 'ActionCombination' then points to 'AndAction Combination' and 'OrAction Combination'. There are also arrows pointing upwards, indicating relationships back to parent objects. For instance, on the right side, arrows point from 'IndependentAction' and 'ActionCombination' back to 'ActionObject', and from 'AndAction Combination' and 'OrAction Combination' back to 'ActionCombination'. A similar pattern of upward-pointing arrows exists on the 'TriggerObject' side of the diagram.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule
Type: figure
Figure: Figure 8. Schematic diagram of the conceptual hierarchy of knowledge inference rules.

Figure 8. Schematic diagram of the conceptual hierarchy of knowledge inference rules.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > Knowledge Extraction from Unstructured Data
Type: text

The dynamic disaster prediction of forest fires and geological landslides requires high spatio-temporal resolution of land cover data. This paper uses Gaofen-2 satellite remote sensing images with the resolution of 0.8 m as the data source. The spatial distribution of disaster-bearing bodies such as buildings and roads is extracted by deep learning methods. The spatial distribution of surface vegetation is obtained by NDVI numerical calculation method [1,19]. It converts the spatial information into triples according to the specification of spatio-temporal and professional attribute representation defined by the concept layer. The triples are stored in the graph database *GraphDB*.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 2. Knowledge Extraction from Semi-Structured Data
Type: text

The terrain data for dynamic disaster prediction of forest fires and geological landslide is the raster geographic data with GeoTIFF format. It converts all types of raster data into vector data of surface elements. It converts geological and lithological data including stratigraphic age, fault, and lithology distribution data into vector geographic information with SHP format. For all types of vector geographic information, it converts the spatial information and feature attributes into triple that are stored in *GraphDB*.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 3. Knowledge Extraction from Structured Data
Type: text

The meteorological data for the dynamic disaster prediction of forest fires and geological landslide is multi-field structured data, which has a direct mapping relationship with the spatio-temporal and professional attributes. Since the vast majority of meteorological data is normal non-hazardous data, this paper only converts the spatial information element attributes of potential disaster-causing meteorological indicators into a triple, so as to avoid the low reasoning speed caused by a large amount of irrelevant data to the reasoning. The triple is stored in *GraphDB*.

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 4. Knowledge Extraction from Disaster Prediction Reasoning Criterion
Type: text

This paper introduces the knowledge extraction method by taking the geological landslide prediction model as an example. It is hard to build a general prediction model for a geological landslide because a single model with idiosyncratic conditions cannot deal with the rich types of geological landslide prediction models and the involved data. This paper defines the inference rules for the geological landslide disaster prediction according to the geological landslide risk probability calculation using machine learning and the effective precipitation models. Based on the general law of geological landslide risk analysis [20], this paper selects the H-index shown in Formula (1) as the metric for the quantitative evaluation of the geological landslide risk:

$$H = P_{(T)} \times P_{(S)} \times P_{(I)}, \tag{1}$$

where H is the risk probability of geological landslide disasters within a specific time and space range;  $P_{(T)}$  is the time probability, i.e., the probability of geological landslide disasters occurring within a specific time range;  $P_{(s)}$  is spatial probability, i.e., the probability of geological landslide disasters occurring within a specific spatial range;  $P_{(I)}$  is the intensity probability, i.e., the intensity of possible geological landslide disasters.

This paper introduces a time probability model based on dynamic precipitation data. The model is mainly composed of two parts. One is the effective precipitation; another is the fitting relationship between the effective precipitation and the frequency of geological landslides. The effective precipitation R in the region considers the respective precipitation contributions of the previous days to the cumulative precipitation that causes the geological landslide. The later the time, the greater the contribution rate to the geological landslide.

$$R = R_0 + \alpha_1 R_1 + \ldots + \alpha_n R_n, \tag{2}$$

where  $R_0$  is the daily precipitation;  $\alpha$  is the contribution rate; n is the number of precipitation days, and  $R_n$  is the precipitation on the n-th day before. A linear fitting relationship between the effective precipitation and the frequency of geological landslides is defined for the time probability of geological landslides associated with dynamic precipitation data.

The geological landslide strength is considered as a parameter representing the destructive force of the geological landslide. It includes the volume of the geological landslide, and the sliding velocity. At present, there is no unified set of indicators to describe them. In this paper, the geological landslide volume parameter is used to obtain the geological landslide strength, and the formula is as follows:

$$m_L = log V_{L}, \tag{3}$$

where  $V_L$  is the volume of a single geological landslide with the unit of m3; which is calculated based on DEM, and  $m_L$  represents the strength of a single geological landslide. By calculating the strength of each geological landslide in the study area by the above formula, it can obtain the statistics of geological landslide strength according to the frequency.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 4. Knowledge Extraction from Disaster Prediction Reasoning Criterion
Type: text

. In this paper , the geological landslide volume parameter is used to obtain the geological landslide strength , and the formula is as follows : $ $ m_L = log V_ { L } , \ tag { 3 } $ $ where $ V_L $ is the volume of a single geological landslide with the unit of m < sup > 3 < / sup > ; which is calculated based on DEM , and $ m_L $ represents the strength of a single geological landslide . By calculating the strength of each geological landslide in the study area by the above formula , it can obtain the statistics of geological landslide strength according to the frequency .

There are many common factors that lead to forest fires and geological landslides. Certain triggering factors can be monitored when forest fires and geological landslides occur. The development of the two types of disasters will develop and spread within a certain time and space range. Affected by the attribute characteristics of surface elements, both disasters will show certain change patterns when they dynamically migrate and change in space over time. Therefore, this paper extends the geological landslide risk calculation formula to forest fires by considering the above-mentioned commonalities of the two disasters. It builds the unified prediction model for forest fire according to Formula (1). Based on Analytic Hierarchy Process, it builds the forest fire-driven comprehensive index for forest fire risk prediction model.

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data
Type: text

When a specific attribute of the external dynamic data satisfies the numerical conditions defined by the rule, it is regarded as an abnormal event candidate. Its time range and space range are treated as the basic condition of semantic query. It queries with SPARQL Protocol and RDF Query Language (SPARQL) [21] statements for a geographic entity that has an intersection with the abnormal event candidate and satisfies the reasoning conditions. When querying the spatio-temporal relationship in the graph database, it can quickly locate the affected area of the abnormal event candidate because the graph database is good at efficient depth-first query, as shown in Figure 9. It plays an important role in the emergency field with high response speed requirements.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart
Type: figure
Figure

Image /page/9/Figure/5 description: A flowchart illustrating a process for handling dynamic data to identify and query abnormal events. The process begins at a 'Start' node and proceeds to 'Real-time input of dynamic data (such as meteorological data)'. Next, a decision is made in an oval labeled 'Judging the dynamics Whether the data attribute satisfies the condition'. If the condition is not met ('No'), the process goes directly to the 'End' node. If the condition is met ('Yes'), the flow continues to 'Generate abnormal events to be judged based on the spatiotemporal scope of external dynamic data'. This is followed by 'Based on the abnormal events to be determined and the geographic location affected by the abnormal events Entity Type Generate Query Statement'. The next step is 'With the help of efficient in-depth retrieval of graph database, the area affected by abnormal events can be quickly locked'. This leads to the final process step, 'Query to get the affected geographic entities within the scope of the abnormal event', which then connects to the 'End' node.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart
Type: figure
Figure: Figure 9. Disaster prediction query flow chart.

Figure 9. Disaster prediction query flow chart.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart
Type: text

With the help of the spatio-temporal semantic query of the disaster fusion data, it can quickly find geographic entities located within the temporal and spatial scope of the AbnormalTrigger based on the temporal-spatial intersection relationship in order to filter the buildings and roads affected by the AbnormalTrigger, as shown in Figure 10.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart
Type: figure
Figure

Image /page/9/Figure/8 description: A conceptual diagram on a 2D coordinate system. The horizontal axis is labeled "spatial dimension" and the vertical axis is labeled "time dimension". The diagram features two large, overlapping ovals. The oval on the left is light green and is associated with the green text "Spatio-temporal distribution of buildings and roads", with a green arrow pointing up from the oval to the text. The oval on the right is light orange and is associated with the orange text "Spatial and temporal distribution of AbnormalTrigger", with an orange arrow pointing up from the oval to the text. The overlapping area of the two ovals is colored reddish-pink. A pink arrow points from this intersection to the text below, which reads "Buildings and roads affected by AbnormalTrigger" in pink.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart
Type: figure
Figure: Figure 10. Schematic diagram of the spatio-temporal intersection of anomalous events and geographic entities.

**Figure 10.** Schematic diagram of the spatio-temporal intersection of anomalous events and geographic entities.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart
Type: text

In order to ensure the efficiency of spatial information query in disaster emergency scenarios, this paper builds multi-scale geocoding indexes for multi-source geographic entities in GeoSPARQL. It significantly improves the depth-first query speed of random geospatial information in comparison with the frequent joint query of multiple tables in the relational database.

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Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 1. First-order logical reasoning
Type: text

As defined in the Common Semantic Ontology of Disaster Prediction, surface combustibles include Woodland, Farmland, Building, and Grassland, and Coniferous Forest belongs to Woodland. Based on first-order logical reasoning, it concludes that Coniferous Forest belongs to Surface Combustibles. With the prefix of *hazard*, the above process can be formally defined as shown in Figure 11. Based on these expressions, *(hazard : Coniferous Forest rdf : subClassOf hazard : Surface Combustibles)* can be inferred.

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```


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 1. First-order logical reasoning
Type: figure
Figure: Figure 11. First-order logical reasoning process.

Figure 11. First-order logical reasoning process.


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

This paper takes SWRL rule reasoning as an example. The feasible reasoning on the DPKG includes reasoning of geographic entity from one attribute to other attributes; reasoning from one geographic entity and its attributes to geographic entities and their attributes.

For example, given the forest fire risk entity and its attribute, i.e., the comprehensive forest fire-driven index *fireDangerValue*, the goal is to build an inference rule for the forest fire risk-level prediction. For this task, the domain analysis rule "when fireDangerValue > 3.8, fireDangerLevel is level 3" is formalized as shown as line 1 in Figure 12.

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```


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning
Type: figure
Figure: Figure 12. Production reasoning process.

Figure 12. Production reasoning process.


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

One more example, knowing the value of aspect entity and aspect attribute, the goal is to construct an inference rule to infer the upslope aspect value. For this task, the domain analysis rule "When aspect is  $<180^{\circ}$ , upAspect = aspect +  $180^{\circ}$ " is formalized as shown as line 2 in Figure 12.

The prediction can be carried out using the SWRL reasoning engine with the inference rules.


Document: 1. Introduction
Section: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 3. Spatio-temporal semantic reasoning
Type: text

This paper builds an automatic real-time monitoring mechanism for emergency information for disaster prediction. It extracts the dynamic information related to the occurrence and development of disasters, i.e., attributes such as space, time, and status of target objects, from structured, semi-structured, and unstructured data sources. It encapsulates the extracted information into real-time event message objects with GeoJSON format. The update of dynamic data will trigger the automatic judgment by the DPKG as shown in Figure 13. The results drive the chained reasoning workflow defined by the disaster prediction inference rules.

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