Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.3. Method for flood influencing factors using Information gain ratio and multicollinearity test
Type: text

Primarily, the evaluation of the flood influencing parameter's importance or the ability for flooding is critical before the training and validation checking of the model (Sameen et al., 2020). The quantification of the importance of each collected parameter has been derived based on their statistical properties and correlation with the flooding. To identify the influential parameters for the prediction of FSMs, the Information Gain Ratio (InGR) technique (Bui et al., 2020a) has been utilized. For measuring the importance of every influencing factor, an InGR value is allocated. The higher InGR values are associated with, the more significant influencing factor. The InGR model is chosen to be employed in the present study due to its simplicity and efficacy and calculated using Eq. (4).

$$\begin{aligned} \text{Gain ratio}(x,Z) = & \frac{\text{Entropy }(Z) - \sum_{1}^{n} \sum_{\substack{i=1 \ |Z|}}^{n} \frac{|Z_i|}{|Z|} \text{ Entropy }(Z_i)}{-\sum_{i=1}^{n} \frac{|Z_i|}{|Z|} \log \frac{|Z_i|}{|Z|}} \end{aligned} \tag{4}$$

where from the training point Z with  $Z_i 1 = 1, 2, 3, .... n$  subsets, the attribute x is belonging.

Several multi-collinearity tests, such as correlation coefficients of Pearson (Xu and Li, 2020), variance decomposition proportions (Zhang et al., 2019), conditional index (Al-Quraishi et al., 2020), and Variance Inflation Factors (VIF) and tolerances (Javidan et al., 2020) have been used to quantify influencing factors for all probability models. In this study, we have utilized the Pearson's correlation coefficient and VIF for quantifying the importance of twelve flood conditioning factors. The VIF >9 and very low correlation shows the problem of multicollinearity in the factors (Duque and Aquino, 2020). It is, thus, highly recommended to exclude the conditioning factor for modelling, which has the VIF >9.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.1. Artificial Neural Networks (ANN)
Type: text

In the present study, with a Back-Propagation (BP) and errorcorrection learning algorithm, the ANN model having three layers (input, hidden and output layers) has been used and successfully employed in the flood susceptibility modelling (Bui et al., 2019b; Zhao et al., 2020). In this study, for the input layers and ten hidden nodes, which are identical with the numbers of the critical parameters were set (Table 1). On the other hand, one node has been employed for the output layer, which is coded as 1 for flood events and 0 for non-flood. Many algorithms are available for the training of an ANN model; however, the most popular one is BP. Thus, for estimating the nonlinear relationship between the critical parameters and the events of the flood, the BP based ANN model has been applied (Dodangeh et al., 2020). Firstly, the initial weights are chosen randomly by BP. The comparison between computed and observed values have taken place. The differences between computed and observed value can be termed as an error. It has been evaluated by several error measures techniques like mean squared error (MSE), root mean square error (RMSE) (Hou et al., 2020). Based on a rule of generalized delta (Rumelhart et al., 1986), the adaptation of initial weights occurs to allocate the total error among the neurons in the network (Pijanowski et al., 2002). The iteration of this process occurs, while the level of error is at a lower level.

data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.2. Support Vector Machine (SVM)
Type: text

A widely popular machine learning model is the SVM, along with a set of linear indicator functions, which has been employed for the issues of function determination, is introduced by Vapnik (2013). For the data transformation in the SVM model, the function of the kernel mathematical system has been used. By using the training datasets, a hyper-plane has been generated, when the conversion from actual SVM datasets to high dimensional feature space has occurred (Choi et al. 2020). For the differentiation of the actual output space, the best linear hyper-plane has been applied. It also used to categorize the data into two classes, such as the susceptibility of non-flood and flood {0, 1}. The capability of the SVM model mainly depends on the suitable kernel functions, e.g., polynomial kernel (PL), sigmoid kernel (SIG), radial basis function (RBF) and linear kernel (LN). Several studies (Tien Bui et al., 2012; Tehrany et al., 2015) reported that the RBF outweighs among different kernel functions for flood susceptibility modelling, which is selected as a benchmark kernel function. Due to the flexibility of radial basis kernel to deal with various dimensionalities of the dataset and its better generalization ability, it has been applied mainly for modelling the flood susceptibility (Chen et al., 2020a; Yang and Cervone, 2019). The significant limitations of modelling with SVM are commonly related to its difficulty in capturing vital parameters (Choubin et al., 2019).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.2. Support Vector Machine (SVM)
Type: table
Table: Table 1 The parameters of the machine learning algorithm used for flood susceptibility modelling

| Model name                   | Description of parameters                                                                                |
|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Artificial neural network | Hidden layer-6, learning rate-0.28, momentum-0.17, seed-4, training time-500, validation threshold-20, Normal to binary filter-TRUE                                              |
| Random forest                | Batch size-100, seed-5, number of iteration-150, max depth-2, calc out of bag-TRUE, Compute attribute importance-TRUE                                                            |
| Random subspace              | Classifier-J48, max depth- -1, minimum number-2, minimum proportion of the variance-0.002, seed-5, numbers of executions slots-1, number of iteration-20, subspace size-0.45     |
| Support vector machine    | Complexity parameter-1, epsilon-1.0E-12, kernel- radial basis function, random seed-5, number of fold- -1, tolerance parameter-0.001, check turned off-True, Calibrator-Logistic |
| Dagging                      | Classifier-M5P, max depth- -1, minimum number-2, a minimum proportion of the variance-0.001, seed-3, numbers of fold-10, verbose-TRUE                                            |


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.3. Random forest
Type: text

The Random forest (RF) approach, is the aggregate of decision trees for classification or regression to predict, was pioneered by Breiman (2001). It is a popular ensemble-learning algorithm (Rahman et al., 2019). The RF is divided into two subsets; Breiman's "random subspace random subspace random subspace random subspace" idea is the first one, and the second is Ho's "random selection features. The Random subspace is an ensemble machine learning process, which utilizes and improves the accurate prediction of a weak classifier by the creation of a set of classifiers. The RF generates various sets of samples by sampling with replacement and creates multiple matching regression tree training phases and then estimates the data classification based on the voting outcomes of multiple classifiers. In the end, a classification with a considerable number of votes over all the trees in the forest has been found and selected by RF.

In the training phase of the regression tree, the rules based on the response parameters were developed to categorize the observation datasets until the prediction has the lowest possible node deviation. One of the significant problems with regression trees is that it tends to outfit the training detests and, thus, performs inadequately when given an uncertain dataset (Criminisi and Shotton, 2013). RF is a feasible way to overcome this weakness. When each regression tree has been trained in the RF algorithm, a portion of the input records and predictor parameters have been randomly chosen as input to the training phase. A set of regression trees has been built after multiple sampling practices, and each set of regression trees is only a training outcome for a randomly chosen subset. It is not advisable to apply a full sample to train the decision trees, because the full sample training neglects the laws of local samples. In the case of flood susceptibility to make the comparison with the outcomes of a new hybrid model, the model which is applied as one of the benchmark models is the RF model.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.4. Random subspace
Type: text

Ho (1989) introduced the Random subspace model as a new ensemble machine learning algorithm for solving environmental problems. On modified feature space, the several classifiers of this model are integrated and trained. For the classifiers, which are training base, this model creates several training subsets (Ho, 1998). Unlike other ensemble models, it uses several samples on feature space, rather than the instance space (Havlíček et al., 2019). This method gets the benefit from the bootstrapping and the aggregation. The training set x, the base classifier w, the number of subspaces L, and the number of subspaces is the four inputs of RS (Luo et al., 2019). The L subsets of ds features have been produced randomly and gathered in subspace, in RS. The base classifier has been trained for generating L different classifiers in subspace on each of the subsets. For producing an ensemble classifier E, these classifiers are combined. In Kuncheva et al. (2010), a detailed description of the algorithm is given. For avoiding the problems regarding over-fitting and to deal with the datasets with the most unnecessary feature, it is highly recommended to use this method (Pham et al., 2017b).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.5. Dagging
Type: text

Dagging was first pioneered by Ting and Witten (1977), which is a renowned re-sampling ensemble model applying most votes for the integration of different classifiers to get better precision in prediction for the base classifiers (Zhao et al., 2013; Walia and Kumar, 2019). The application of Dagging ensemble technique in the field of flood susceptibility is rare. However, this model has been broadly employed in the fields of medicine (Hosni et al., 2019), banking (Sawant and Bhurchandi, 2019), and recently, in landslide susceptibility modelling (Pham et al., 2017a). Instead of creating the bootstrap samples to obtain the base classifier, it creates several separate samples. It is considered a

promising machine learning algorithm for classification in recent decades. The Dagging ensemble model has been applied for solving numerous problems regarding classification in the case of the real world. For a particular training dataset including N samples, the creation of an M dataset can occur and come from the actual training datasets. Every dataset has several n (n<N) samples that are unique from each other. In Dagging, the technique of sampling which is utilized in this. The samples are not replaced in the actual training datasets and can be selected as a part of a specific dataset where the number of sample datasets is increased. Accordingly, on every sample dataset, a base classifier was built. In the end, several classifiers will be gained based on the training dataset.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.5. Precision appraisal
Type: text

Either a positive or negative pixel has been included in each class of flood susceptibility mapping classification. The real positives and true negatives are referred to as those positive (flood) and negative (nonflood) pixels, which are divided accurately into positive or negative classes. In contrast to this, false positives and false negatives are referring to those positive and negative points which have been mistakenly divided (Schumann et al., 2014). The area has explained the precision of two classes in a receiver operating curve (ROC) graph under the ROC curve (AUC) value. This method has been employed for validating the five flood susceptible models. Hence, by presenting the 1-specificity on X-axis against sensitivity on Y-axis, the ROC curve has been plotted. The sensitivity indicates the sum of the pixels accurately divided as the pixels of the flood. Also, the 1-specificity represents the several non-flood pixels accurately divided, by utilizing the following Eq. (5) the value of AUC can be calculated (Tien Bui et al., 2018).

$$AUROC = \frac{\dot{a} TP + \dot{a} TN}{P + N}$$
 (5)

Where the AUC has been calculated to appraise the model's overall performance when categorizing indefinite data, in general, AUC varying from 0.5 to 0.6 donates an incompetent model. In contrast, the AUC value between 0.6 and 0.7 indicates a poor performance model. Along with it, a model with 0.7 to 0.8 AUC value demonstrates moderate performance. When the AUC value greater than 0.8 indicates a high fitness model with the dataset (Sajedi-Hosseini et al., 2018). In addition to the AUC value, the root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) was applied for evaluation and comparison of model outcomes in this study.

To appraise the accuracy of flood susceptibility modelling, several non-parametric statistics have been applied in this study. Any statistical inference is not required for non-parametric models (Derrac et al., 2011). The non-parametric tests, such as the Friedman test and paired t-test (Friedman, 1937) were adopted when the distribution of the data was normal (Martínez-Álvarez et al., 2013). These tests have been employed to compare the significant differences between two or more models (Beasley and Zumbo, 2003). At the 5% (p<0.5) (null hypothesis) significance level, the flood model's performances are similar, and this is the first inference for running this test. The outcomes were not applied, in the case of true p-values for all the models in the Friedman test were >0.5 (Bui et al., 2018).

The Wilcoxon signed-rank test was conducted to verify the flood models by evaluating the statistical significance of systematic pairwise differences among the models. In the case of the five susceptibility models, the p-value and the z-value were utilized to evaluate the significance of differences among them to make sure of the statistical significance. The null hypothesis will be rejected if the value of p is < 5%, and the value of z is more than the critical values of z (+1.96 and -1.96).

2. Material and methods
2.5. Precision appraisal


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.5. Precision appraisal
Type: figure
Figure

Image /page/9/Figure/2 description: A vertical bar chart showing the 'Average Merit' for different 'Flood conditioning parameters'. The y-axis, labeled 'Average Merit', ranges from -0.1 to 0.6. The x-axis, labeled 'Flood conditioning parameters', lists ten categories: LULC, Distance to road, Elevation, Slope, TRI, SPI, STI, Curvature, TWI, and Aspect. Each bar is blue and includes a black error bar. The bar for LULC is the tallest, with an average merit of approximately 0.52. The next three bars, 'Distance to road', 'Elevation', and 'Slope', have similar heights, with average merits of about 0.12, 0.11, and 0.11, respectively. The remaining parameters have much lower average merits, all below 0.05: TRI (approx. 0.03), SPI (approx. 0.03), STI (approx. 0.015), Curvature (approx. 0.01), TWI (approx. 0.01), and Aspect (approx. 0.005).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.5. Precision appraisal
Type: text

Fig. 6. The importance of flood conditioning factors using the information gain ratio, the standard error bars show the accuracy of the results.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.1. The influential factors in modelling flood susceptibility by InGR and multicollinearity tests
Type: text

The analysis result is presented in Fig. 6, where the values of the InGR of each parameter are calculated using a 10-fold cross-validation technique. The findings of InGR showed that the LULC (0.52), DR (0.12), elevation (0.11), and slope (0.495) are the more significant with the high InGR values as compared to other factors of influence. Total roughness index TRI (0.03), stream power index SPI (0.03), and sediment transport index STI (0.02) demonstrate moderate relevancy. Similarly, the total wetness index, TWI (0.01), and curvature (0.01) show low relevance. It is noteworthy; the aspect factor exhibits the value of InGR= 0.00; therefore, it is a less influencing factor for predicting FSMs.

The studied river basin is predominated by agricultural land near the main river. In contrast, the vegetation occupies 55% of the total area features. For the flash floods incident, these features are most favourable (lcyimpaye, 2018). Also, for the occurrence of floods, the most recognized significant features are the elevation and slope. This is much found and explained in the works of Tien Bui et al. (2019b). The uneven topography with lower elevation and curvatures in convergent pattern and low slope angle causes a higher chance of flooding in the study area. However, for the occurrence of flood, there is no predictive value in case of an aspect due to the flooding point distribution in the directions of the slope is flat and smooth.

The outcomes of multicollinearity diagnostics tests (Table 2) reveal that elevation with the largest VIF (2.71), followed by rainfall, STI, SPI, slope, soil type, distance to the river, TRI, TWI, LULC, curvature, and aspect respectively. The results also indicate that there is no collinearity problem among 12 potential factors for involving flood susceptible models. Additionally, Pearson's correlation between flooding and its

potential influencing factors are presented in Table 3. According to the prior research (Douglas et al., 2000), the probability of flood occurrence is increasing with higher correlation values. The critical potential factors for the occurrence of the flood were distance to the river, followed by elevation, soil type, rainfall, aspect, curvature, TWI, LULC, slope, SPI, STI, TWI. In this immediate area of study, the majority of floods occurred where the distance to the river is low (0-100) m, the soil type is aquafssol, the elevation is 18–69 m, the curvature is >0.327, land cover class have bare land, SPI is high, STI is high, rainfall ranges from 361 to 550 mm, the slope belonging from 0–5°, TWI belonging from – 1.54 to 7.72, TRI is high, and the aspect is flat.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.2. The analysis of flood susceptible models
Type: text

The five machine learning models, such as ANN, SVM, RF, subspace random, and Dagging, were employed to compute the maps of flood susceptibility for each pixel in the basin. The Dagging model has been proved as the high-performance model of prediction among all the benchmark models for the geospatial datasets obtained based on the above results of the experiment. Several techniques, such as natural break, equal interval, quantile, regular interval, standard deviation, and manual technique, are available for reclassifying the flood susceptible models in the ArcGIS 10.3. Among these techniques, quantile and natural break methods have been widely reported in the available literature of flood susceptibility studies (Tehrany et al., 2015; Tien Bui et al., 2019b). The quantile is one of the famous reclassifying techniques which is able to provide better outcomes in comparison to other reclassifying techniques, and it has been mostly used for reclassifying the flood susceptible maps (Tehrany et al., 2015). For this reason, in this present study, the quantile reclassifying approach was selected. The flood susceptibility models were reclassified into five classes such as very low, low, moderate, high and very high (Fig. 7).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.2. The analysis of flood susceptible models
Type: table
Table: Table 2 The evaluation of the flood conditioning factors using a multi-collinearity test (VIF).

| Parameters                      | Elevation | Aspect | Curvature | Slope   | Topographic roughness index | Topographic wetness index | Stream power index | Sediment transport index | Soil types | Distance to river | Rainfall | Land use land cover |
|---------------------------------|-----------|--------|-----------|---------|-----------------------------------|---------------------------------|--------------------------|--------------------------------|------------|----------------------|----------|---------------------------|
| Multicollinearity test (VIF) | 2.7128    | 1.0786 | 1.08072   | 1.33623 | 1.196                             | 1.1935                          | 1.56453                  | 1.6207                         | 1.31539    | 1.29923              | 2.67087  | 1.10026                   |

Parameters
Elevation
Aspect
Curvature
Slope
Topographic roughness index
Topographic wetness index
Stream power index
Sediment transport index
Soil types
Distance to river
Rainfall
Land use land cover


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.2. The analysis of flood susceptible models
Type: table
Table: Table 3 The Pearson's correlation coefficient between flooding and its potential factors.

| Influential factors | Distance to river | Elevation | Soil types | Rainfall | Aspect | Curvature | Topographic wetness index | LULC   | Slope  | Stream power index | Sediment transport index | Topographic roughness index |
|------------------------|----------------------|-----------|---------------|----------|--------|-----------|------------------------------|--------|--------|-----------------------|-----------------------------|--------------------------------|
| Correlation (Pearson)  | 0.6024               | 0.4519    | 0.4429        | 0.3463   | 0.2325 | 0.1417    | 0.0947                       | 0.0662 | 0.0435 | 0.0359                | 0.0289                      | 0.0169                         |

Influential factors
Distance to river
Elevation
Soil types
Rainfall
Aspect
Curvature
Topographic wetness index
LULC
Slope
Stream power index
Sediment transport index
Topographic roughness index


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.2. The analysis of flood susceptible models
Type: text

The five flood susceptible models having five classes have been represented in Fig. 7. The findings showed that, in the case of Dagging model, the smallest area percentage (14.03%) comes under the very high class, followed by low (14.89%), high (15.61%), moderate (23.78%), and very low (31.70.42%) classes. The percentages of area are 30.20%, 16.22%, 13.32%, 16.88%, and 23.39 % for the very low, low, moderate, high, and very high classes, respectively, in the case of the SVM model. In the case of RS, RF, and ANN models, the area percentages for very high flood susceptible classes are 21.70%, 29.93%, and 33% (Fig. 8). The very high flood susceptibility class has occupied the highest area (1160 km2; indicated by red colour), and the very low flood susceptible class has occupied the smallest area (510 km2; indicated by dark green) in case of Dagging hybrid model. In the case of the SVM model, the maximum area (1120 km2) has come under the very high flood susceptible class, while the moderate flood susceptible class has covered the minimum area (500 km2). Out of the total area 1020, 1090, and 90 km2 areas have come under the very high flood susceptibility class of the RS, RF, and ANN models respectively. In the class of low flood susceptibility, the areas are 780, 1130 and 1230 km2.

It is obtained in all maps, in the areas of a flat plain which is close to the main river, which has very high and high flood susceptible areas. The identical results were found in the works of Tehrany et al. (2015). Also, in the eastern and south-eastern parts of the river basin, the very high and high susceptible classes were mainly located. Almost the same spatial distribution pattern of flood susceptible areas has been found in the case of all five models in terms of area under high and very high class, though in the case of the Dagging model, the percentages (14.03%) is comparatively smaller than the other models. This

indicates a more practical outcome related to the Dagging hybrid model than other models, with greater accuracy and dependability. This outcome may be time decreasing and costing of a capable mollification planning in the Teesta river basin targeted for land-use plan and policy implication.

The region surrounding the river basin and the main channel of the river were majorly susceptible to flood. They were in the class of very high susceptibility as depicted by the map of flood susceptibility. This outcome was also according to the results of Yang et al. (2015) in Hainan Province, China. The settlement area of the study areas, indicated by red colour (Fig. 5a), was found in the same region (eastern part of the study area) where very high and high flood susceptible areas were located (Fig. 7). Yang et al. (2015) also reported that the identical spatial pattern of flood susceptible zones and urban areas were found in their working area. Yang et al. (2015) stated that the flooding has mostly harmed the world's approximately 0.18 million people at annual scale in those regions, which are nearer the rivers and mostly dominated by urban and semi-urban areas.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: text

We have validated the five flood susceptible models using RoC based on the validation and training points (Fig. 9). The validation for the flood susceptible maps has analysed, which were generated utilizing the models through the datasets of training point (for flood existing the success rate curve) and validation point (for anticipated flood event the prediction rate curve). That is why, on the maps of flood susceptibility, the points of flood validation and training were positioned. The Area

3. Results
3.3. Validation and comparison


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: figure
Figure

Image /page/10/Figure/10 description: A figure displaying five maps, labeled (a), (b), (c), (d), and (e), each illustrating 'Flood Susceptibility' for the same geographical region. The maps use a color gradient to indicate the level of risk. A legend on the right side of the figure defines the color scheme: dark green for 'Very low', light green for 'Low', yellow for 'Moderate', orange for 'High', and dark red for 'Very high'. All five maps show a similar pattern with a central, elongated area of high to very high susceptibility (orange and red), surrounded by areas of low and very low susceptibility (green) along the irregular borders. The extent and intensity of the high-risk areas vary slightly across the five maps. Below the legend, a scale bar indicates distances from 0 to 80 km, with markings at 0, 10, 20, 40, 60, and 80 km.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: text

Fig. 7. Flood susceptibility models using (a) Artificial neural network, (b) Support Vector machine, (c) Random forest, (d) Random subspace, and (e) Dagging,


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: figure
Figure

Image /page/11/Figure/2 description: A clustered bar chart compares the area distribution across different flood susceptible zones as predicted by five different models. The y-axis is labeled "Area (km²)" and ranges from 0 to 1600. The x-axis is labeled "Flood susceptible zones" and has five categories: Very Low, Low, Moderate, High, and Very high. The legend indicates the five models: ANN (blue), RF (red), Random subspace (green), SMO (purple), and Dagging (teal). The approximate data for each zone is as follows: For the 'Very Low' zone, the areas are: ANN ~80, RF ~1100, Random subspace ~1020, SMO ~1120, and Dagging ~1160 km². For the 'Low' zone, the areas are: ANN ~1350, RF ~580, Random subspace ~540, SMO ~600, and Dagging ~550 km². For the 'Moderate' zone, the areas are: ANN ~520, RF ~360, Random subspace ~600, SMO ~490, and Dagging ~880 km². For the 'High' zone, the areas are: ANN ~500, RF ~510, Random subspace ~720, SMO ~620, and Dagging ~580 km². For the 'Very high' zone, the areas are: ANN ~1220, RF ~1100, Random subspace ~800, SMO ~860, and Dagging ~510 km².


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: text

Fig. 8. The flood susceptible area under different classes of five machine learning models.

can explain the accuracy of the models Under Curve (AUC). The higher the AUC, the higher the model's performance to predict. According to the success rate curve's (training dataset) outcome, the Dagging model (AUC = 0.87) outperformed the RF (AUC = 0.86), RS (AUC = 0.85), SVM (AUC = 0.84), and ANN (AUC = 0.83) models (Fig. 9a). Similarly, the outcomes of the curve of prediction rate by utilizing validation dataset (Fig. 7b) the AUC values for the Dagging model is 0.873, which outweighs the SVM (AUC = 0.86), RS (AUC = 0.83), ANN (AUC = 0.83), and RF (AUC=0.81). The results suggest that though the five FSMs had high prediction accuracy (AUC >0.80), the Dagging model is the best for the flood susceptibility modelling in the case of this study area, as the accuracy of prediction from 0.86 to 0.87 was enhanced by this model


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: text

prediction rate by utilizing validation dataset ( Fig . 7b ) the AUC values for the Dagging model is 0 . 873 , which outweighs the SVM ( AUC = 0 . 86 ) , RS ( AUC = 0 . 83 ) , ANN ( AUC = 0 . 83 ) , and RF ( AUC = 0 . 81 ) . The results suggest that though the five FSMs had high prediction accuracy ( AUC > 0 . 80 ) , the Dagging model is the best for the flood susceptibility modelling in the case of this study area , as the accuracy of prediction from 0 . 86 to 0 . 87 was enhanced by this model

The analytical performance of five flood susceptible models based on the training and testing data using several error measures like root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination  $(R^2)$  are presented in Table 4. The finding clearly showed that the proposed Dagging model had the lowest RMSE= 0.19, lowest MAE=0.08, and very high  $R^2 = 0.84$  compared to other benchmarked models for the training stages (Table 4). In the training stage, the result of statistical performance for the models (five) follows the decreasing order: Dagging>ANN>FR>SVM>RS. Likewise, in the training stage, in the validation phase, the Dagging ensemble model showed the lowest RMSE = 0.18, lowest MAE = 0.08. Very high  $R^2$  = 0.85, which represents a very high level of satisfaction in case of FSMs along with fewer errors. The performance analysis of the five models in the validation stage follows the descending order: Dagging> RF>ANN>SVM> RS. Overall, the Dagging hybrid model had the best performance, while the least achievement is in the case of random subspace for the mapping of flood susceptibility regarding the spatial prediction by the use of both the datasets of training and validation. In fact, for this study, the Dagging model has been found as the most dominant ensemble hybrid model that performed with the higher accuracy of prediction among all models. For the evaluation of the flood susceptibility mapping, this outcome is substantially agreed, and the method of the hybrid ensemble model is appropriate, logical, and reliable, which was confirmed by Tehrany and Jones (2017). By using the Friedman test (Ipe, 1987) and Wilcoxon signed-rank tests (Hong et al., 2019a) at the level of 95% confidence (Tables 5 and 6), the considerable differences among the FSMs of five models were computed. In Tables 5 and 6, the outcomes of the Freidman test and the Wilcoxon signed-rank test are revealed. The findings of Friedman's test showed that the confidence level (or p-value) was < 0.05, which suggested to accept the primary assumption and reject the null hypothesis because, at the level of 95% confidence, the considerable difference among the benchmark models was absent. The values of chi-square ( $\chi^2$ ) and p-value of five


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: text

95 % confidence ( Tables 5 and 6 ) , the considerable differences among the FSMs of five models were computed . In Tables 5 and 6 , the outcomes of the Freidman test and the Wilcoxon signed - rank test are revealed . The findings of Friedman ' s test showed that the confidence level ( or p - value ) was < 0 . 05 , which suggested to accept the primary assumption and reject the null hypothesis because , at the level of 95 % confidence , the considerable difference among the benchmark models was absent . The values of chi - square ( $ \ chi ^ 2 $ ) and p - value of five

FSMs are far-away from 0.05 and 3.841, respectively, which are the standard values (Table 5). Whether any significant differences are present or not among the five FSMs, cannot be detected by the Friedman test. To overcome this disadvantage, many statistical tests are available. However, we have employed the Wilcoxon signed-rank test to investigate whether statistical differences are present or not between any pairs of FSMs. If the p-value and z value were lesser than the level of significance (<0.05) and higher than the critical values of z (+1.96 and -1.96) respectively, the null hypothesis must be discarded. There were significant differences in the performance of the FSMs. Also, the results of the Wilcoxon signed-rank test (Table 5) indicates that p-value and z value are both distant from the standard values of 0.05 and (from -1.96 to +1.96), respectively for all pairs except for the pairs of Dagging vs. ANN, Dagging vs. SVM, digging vs. RF, Dagging vs. RS, and RF vs. SVM. This implies statistically significant differences among all FSMs. On the other hand, the p values were > 0.05, and the z values exceeded the critical values: therefore, the outcome undoubtedly stated that there are no statistically significant differences in the case of the Dagging model's performance as compared to the other FSMs except for the ANN.

Additionally, to confirm the superiority of the Dagging model over other models, the paired t-test (Altman, 1990) has been performed in this study. It is noted that a significant level of  $\alpha$  is chosen to be p<0.05. If the estimated p-value is less than the significant level  $\alpha$ , the null hypothesis will be rejected that there is no statistical difference between the two paired models. The Dagging model has notably outperformed ANN (p-value =0.0001) and RS (p-value =0.017), except for RF (p-value =0.107) and SVM (p-value=0.263). Thus, based on the statistical t-test, the Dagging model is considered the best fitted for modelling of FSMs in the Teesta River basin.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 3. Results > 3.3. Validation and comparison
Type: text

< 0 . 05 . If the estimated p - value is less than the significant level $ \ alpha $ , the null hypothesis will be rejected that there is no statistical difference between the two paired models . The Dagging model has notably outperformed ANN ( p - value = 0 . 0001 ) and RS ( p - value = 0 . 017 ) , except for RF ( p - value = 0 . 107 ) and SVM ( p - value = 0 . 263 ) . Thus , based on the statistical t - test , the Dagging model is considered the best fitted for modelling of FSMs in the Teesta River basin .

For investigating the Dagging ensemble model's performance, the ANN, RF, RS, and SVM were regarded in this study. To increase the best performance, the accurate measurement of the parameters of these state-of-arts models should be set carefully. In Table 1, the parameters for these models are shown. By the utilization of the best flood influencing factor, SVM, ANN, RS, RF, Dagging hybrid models were used for modelling flood susceptibility prediction. The criteria of appraisal (RMSE, MAE,  $R^2$ - and AUC) were computed for the comparison among the Dagging model and other benchmarked methods based on training and validation datasets.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: text

The accurate estimation of flood susceptibility is an essential process for safeguarding people 's and developing feasible and effective mitigation measures (Sahana et al., 2020). Therefore, flooding management to


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: figure
Figure

Image /page/12/Figure/2 description: The image displays two ROC (Receiver Operating Characteristic) curve plots, labeled A and B. Both plots graph Sensitivity on the y-axis against 1 - Specificity on the x-axis, with scales from 0.0 to 1.0. Each plot compares the performance of five different models and includes a diagonal red reference line.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: text

Plot A shows five curves that are closely grouped, indicating similar performance. The legend, titled "Source of the Curve", provides the Area Under the Curve (AUC) values for each model:
- ANN (blue line): AUC = 0.834
- RF (green line): AUC = 0.863
- Random subspace (light green line): AUC = 0.859
- Dagging (purple line): AUC = 0.871
- SVM (yellow line): AUC = 0.845

Plot B also shows five curves, but they are more spread out than in plot A. The legend provides the following AUC values:
- ANN (blue line): AUC = 0.835
- RF (green line): AUC = 0.811
- Random Subspace (light green line): AUC = 0.839
- SVM (purple line): AUC = 0.865
- Dagging (yellow line): AUC = 0.873

Fig. 9. The validation of five flood susceptible models using RoC based on training point (a) and validation point (b).

prevent and decrease the damages caused by flooding is the primary requirement. To do so, the FSM has become the foundation of management across the world (Sahana et al., 2020; Wang et al., 2020). Therefore, researchers always try to use new and robust techniques to obtain very high precision and accurate results, which will be very

helpful to propose flood management plans (Wang et al., 2019a, 2020; Bui et al., 2020a; Nachappa et al., 2020). This is the reason; we also attempted to apply some advanced models, like ANN, SVM, RF, random subspace and newly proposed Dagging algorithms to prepare flood susceptibility maps for Teesta river basin of Bangladesh. It is indispensable


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: table
Table: Table 4 The accuracy assessment of five flood susceptible models for training and testing data using different error measures.

| Methods | ANN      |            | RF       |            | Random subspace |            | SMO      |            | Dagging  |            |
|---------|----------|------------|----------|------------|-----------------|------------|----------|------------|----------|------------|
|         | Training | Validation | Training | Validation | Training        | Validation | Training | Validation | Training | Validation |
| RMSE    | 0.22     | 0.27       | 0.252    | 0.254      | 0.3             | 0.31       | 0.253    | 0.28       | 0.19     | 0.189      |
| MAE     | 0.121    | 0.16       | 0.172    | 0.173      | 0.2             | 0.203      | 0.152    | 0.17       | 0.081    | 0.084      |
| R2      | 0.774    | 0.718      | 0.712    | 0.738      | 0.67            | 0.599      | 0.696    | 0.677      | 0.839    | 0.852      |

Methods
ANN
RF
Random subspace
SMO
Dagging


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: table
Table: Table 5 The results of the Friedman test for five flood susceptible models with the p value = 0.05.

| Models  | Mean ranks | Chi-square | Significance |
|---------|------------|------------|--------------|
| ANN     | 1.91       | 218.82     | 0            |
| RF      | 3.2        |            |              |
| Rsp     | 3.58       |            |              |
| SMO     | 3.03       |            |              |
| Dagging | 3.28       |            |              |

Models
Mean ranks
Chi-square
Significance


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: text

to model the flood susceptibility in this region as the whole country has experienced frequent flooding for a very long time.

Among the applied approaches, ANN, SVM, and RF have been widely used for landslide susceptibility modelling (Hong et al., 2019b; Fang et al., 2020; Wu et al., 2020b), flood susceptibility modelling (Chen et al., 2019b; Chen et al., 2020c; Ali et al., 2020; Khosravi et al., 2019b), gully erosion (Amiri et al., 2019; Tien Bui et al., 2019a; Amiri et al., 2019) and forest fire modelling (Gigović et al., 2019; Tehrany et al., 2019; Pourghasemi et al., 2020b), but scarce studies applied random subspace and Dagging model for different natural hazards prediction including flood susceptibility. Therefore, the performance of the conventional three models and two new models was evaluated and compared by using the AUC value of the ROC curve at training and testing scale and a series of non-parametric statistics. Also, the influences of the flood conditioning parameters were assessed by using the information gain ratio.

The findings showed that the proposed Dagging models outperformed (AUC for training datasets: 0.87, and AUC for testing data sets: 0.873) the other four models (Pham et al., 2017a; Hong, 2018; Yariyan et al., 2020). However, the other four models also performed better as the AUC value achieved more than 0.8 at both training and testing datasets. Therefore, it is reasonable to conclude that Dagging performed better than other models because it is an ensemble machine learning technique which has been efficiently used for obtaining accurate results for predicting any natural hazards or other environmental components (Pham et al., 2017b). Several scholars already applied Dagging models along with other models for predicting the landslide susceptibility and found highly accurate findings (Pham et al., 2017a, 2020a; Nguyen et al., 2019; Kalantar et al., 2020; Nhu et al., 2020). Nguyen et al. (2020) reported that the Dagging model outperformed the other four ensemble machine learning models like random subspace, random subspace, and cascade generalization for groundwater potentiality modelling. Nguyen et al. (2020) accounted that Dagging was more capable than other algorithms for reducing the variance, bias, and noise of the groundwater potential modelling. In the present study, Dagging performed better for training and testing datasets; therefore, it can be stated that the Dagging model is also highly capable of modelling the flood susceptibility. Therefore, it


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: table
Table: Table 6 The comparison of the performances of the ensemble machine learning models for flood susceptibility using the Wilcoxon signed-rank test.

| Pairwise Comparison            | Positive | Negative | Z value | P-value | Significance |
|--------------------------------|----------|----------|---------|---------|--------------|
| ANN vs. RF                     | 296      | 37       | -12.27  | 0.05    | Yes          |
| ANN vs. Ransdom Subspace       | 269      | 64       | -9.53   | 0.05    | Yes          |
| ANN vs. SMO                    | 225      | 108      | -7.75   | 0.05    | Yes          |
| ANN vs. Dagging                | 239      | 94       | -6.64   | 0.05    | Yes          |
| RF vs. Random subspace         | 247      | 86       | -5.93   | 0.05    | Yes          |
| RF vs. SMO                     | 152      | 181      | 1.83    | 0.01    | No           |
| RF vs. Dagging                 | 198      | 135      | -0.63   | 0.01    | No           |
| Random subspace vs. SMO        | 145      | 188      | 2.73    | 0.05    | Yes          |
| Random subspace vs. Dagging | 143      | 190      | 1.91    | 0.05    | No           |
| SMO vs. Dagging                | 180      | 153      | -1.67   | 0.05    | No           |

Pairwise Comparison
Positive
Negative
Z value
P-value
Significance


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 4. Discussion
Type: text

is highly recommended to apply the Dagging model for other natural hazards prediction for obtaining high precision results.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 5. Conclusion
Type: text

Two new hybrid ensemble models, namely Dagging and the RS, were used for the first time in the present analysis, along with three machine learning models, e.g., the ANN, the RF, and the SVM, to model the flood susceptibility mapping in the Teesta River basin, Northern Bangladesh. This analysis primarily selected a total of 413 flooding points with twelve variables such as elevation, slope, curvature, aspect, SPI, TWI, STI, LULC, rainfall, river width, TWI, and soil types affecting the flooding. Multi-collinearity diagnostic tests (VIF), IGR, and the correlation matrix of Pearson were utilized to measure the relevance of flood conditioning parameters. The effect of the parameters of flood conditioning was measured by the approaches of feature selection, such as the information gain ratio. To verify the flood prone models based on the training and validation datasets, the Friedman test, Wilcoxon signed-rank test, t-paired test, and the RoC curve have been used. In the case of the Dagging model, the highest versatility and predictive capabilities were found, followed by the RF, the ANN, the SVM and the RS model. In the result of validation step, the implementation of the ROC Curve shows that, relative to other methods, the Dagging model has the maximum performance (AUC = 0.863-training stage; AUC = 0.873-validating stage). The standard of all models for the mapping of flood susceptibility, however, was acceptable and reliable. Dagging is one of the most capable methods for flood-susceptible modeling, the results of the study showed. A total area of 29.62 percent was described as extremely vulnerable to flooding as an ideal model. The biggest downside, however, is that for certain variables, like SPI and land use, the implementation of these models does not take into consideration the modifications over time, since these variables are very complex. Future analysis on the temporal scale will be done depending on the availability of temporal databases of these variables. In addition, by conducting sensitivity analysis on different influential variables, these models can be upgraded. Compared to the other models, the dagging model has advantages, including less candidate parameters, high optimization power, and rapid convergence to prepare susceptibility maps for flush

Flood management strategies have recently been considered a top priority, particularly in Bangladesh, where flash floods occur every year. Other basins and areas, however, have not been tested for flood control measures yet. The present research, therefore, was conducted in the Teesta River basin using some sophisticated machine learning algorithms to provide useful knowledge on the methods to be implemented to help the implementation of successful flash flood mitigation strategies and land-use policy planning by local authorities and other parties.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: Appendix A. Supplementary data
Type: text

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2020.09.006.


Document: flood kg
Section: ABSTRACT
Type: text

Increasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage. Researchers have been dedicating to urban flood risk assessments in recent years. However, current research is still facing the challenges of multi-modal data fusion and knowledge representation of urban flood events. Therefore, in this paper, we propose an Urban Flood Knowledge Graph (UrbanFloodKG) system that enables KG to support urban flood risk assessment. The system consists of data layer, graph layer, algorithm layer, and application layer, which implements knowledge extraction and storage functions, integrates knowledge representation learning models and graph neural network models to support link prediction and node classification tasks. We conduct model comparison experiments on link prediction and node classification tasks based on urban flood event data from Guangzhou, and demonstrate the effectiveness of the models used. Our experiments prove that the accuracy of risk assessment can reach 91% when using GEN, which provides a a promising research direction for urban flood risk assessment.


Document: flood kg
Section: CCS CONCEPTS
Type: text

• **Information systems**  $\rightarrow$  *Clustering and classification.*


Document: flood kg
Section: KEYWORDS
Type: text

urban flood, knowledge graph, link prediction, graph neural network


Document: flood kg
Section: ACM Reference Format:
Type: text

Yu Wang, Feng Ye, Binquan Li, Gaoyang Jin, Dong Xu, and Fengsheng Li. 2023. UrbanFloodKG: An Urban Flood Knowledge Graph System for Risk Assessment. In *Proceedings of the 32nd ACM International Conference on*

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© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0124-5/23/10...\$15.00 https://doi.org/10.1145/3583780.3615105

Information and Knowledge Management (CIKM '23), October 21–25, 2023, Birmingham, United Kingdom. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3583780.3615105


Document: flood kg
Section: 1 INTRODUCTION
Type: text

Increasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage [58]. With the rapid development of machine learning, deep learning methods have been increasingly explored for their potential application in urban flood risk assessment. Existing research has demonstrated that Long Short Term Memory (LSTM) [56], Recurrent Neural Network (RNN) [3] and Convolutional Neural Network (CNN) [30] are commonly used in flood forecasting. However, the structures of the above models are complex and the explainable ability is poor. Therefore, it is worth exploring how to use knowledge related to urban flood events to help human capture the relationships between time and space, overcome the limitations of above models, and further improve the accuracy of risk assessment.

In addition, recent studies emphasize the importance of data fusion in specific applications, such as data fusion [16] is proposed to improve the accuracy of hydraulic simulation of urban flood, and a remote sensing and text bimodal data fusion model based on UFCLI [49] is proposed to improve accuracy of urban flood damage. However, no researchers try to fuse multi-modal data for urban flood risk assessment. Meanwhile, some studies try to extract explicit knowledge from data for performance enhancement and semantic reasoning [6, 54, 55]. But in the field of flood assessment, there are mainly data-driven researches [30, 38], and remains a lack of effective representation of knowledge to aid in flood risk assessment. Considering existing research has not explored how to integrate multi-modal data into knowledge from urban flood to support downstream tasks, we summarize the key challenges for further research on urban flood risk assessment from two aspects:

• Multi-modal Data Fusion. Due to the diverse sources and tools of data collection, the collected data has different structures and forms. On the one hand, urban flood related data is usually stored in different structures, such as tables, texts, and images. On the other hand, there are many relationships between data, such as the similar duration of event A

- and event B. Therefore, how to conduct effective fusion of multi-modal data has not been fully explored.
- Knowledge Representation. Urban flood events involve data from multiple aspects, including flood table data organized by monitoring departments, textual data reported in news broadcasts, on-site image data collected by sensors, etc. For this multi-modal data, how to embed multi-modal data related to urban flood events to downstream task-driven algorithm models is a challenge.

Knowledge graph (KG) [34] is a technology that represents and stores entities and relationships in a graph form and has been successfully applied in fields such as KG-based recommendation systems [13] and KG-based question answering systems [17]. Specifically, KG uses triple facts (head entity, relationship, tail entity) to store and represent knowledge in the real world, where entities can be objects, events, locations, or abstract concepts, and relationships describe their connections [39]. Meanwhile, Liu [23] combines urban computing with KG system, exploring new modes of urban data management and analysis. Therefore, we believe that this provides a reasonable and feasible way for risk assessment of urban flood events.

To unlock the full potential of urban flood data for efficient risk assessment, we present a system called Urban Flood Knowledge Graph (UrbanFloodKG). The system consists of data layer, graph layer, algorithm layer and application layer to meet various needs in urban flood risk assessment research. Finally, we conduct link prediction and node classification experiments by using different models to demonstrate the feasibility of the proposed UrbanFloodKG system.


Document: flood kg
Section: 1 INTRODUCTION
Type: text

Meanwhile , Liu [ 23 ] combines urban computing with KG system , exploring new modes of urban data management and analysis . Therefore , we believe that this provides a reasonable and feasible way for risk assessment of urban flood events . To unlock the full potential of urban flood data for efficient risk assessment , we present a system called Urban Flood Knowledge Graph ( UrbanFloodKG ) . The system consists of data layer , graph layer , algorithm layer and application layer to meet various needs in urban flood risk assessment research . Finally , we conduct link prediction and node classification experiments by using different models to demonstrate the feasibility of the proposed UrbanFloodKG system .

The main contributions of our work are as follows:

- We propose a system that retrieves urban flood-related data from various data sources, construct a KG of urban flood events, and further integrate various KG representation algorithms and graph neural network models. This system is the first KG-based urban flood risk assessment system, providing a new perspective for assessing urban flood events.
- We present a systematic scheme for UrbanFloodKG construction, which identifies the key elements in urban flood environment as entities, and describes their semantic connections as relations. The proposed construction scheme provides a general framework to fuse the urban flood-related data into the KG, and potentially benefits various downstream tasks.
- We construct a dataset for link prediction and node classification tasks using urban flood-related data collected from Guangzhou over the years. After using different models, we compare their performance and demonstrate the effectiveness and feasibility of our proposed system.

In the following sections, we present the design and implementation of our UrbanFloodKG system. Section 2 introduces the background of KG and the requirements for our system. Section 3 provides an overview of the system architecture and details each layer. In Section 4, we present the application scenarios and compare various models for knowledge representation and graph neural networks. Section 5 discusses related work, and we conclude the paper in Section 6.


Document: flood kg
Section: 2 PRELIMINARY & REQUIREMENTS > 2.1 Preliminary
Type: text

Here we formally define the KG as follows [11, 23, 57].

Definition 2.1 (Knowledge Graph). A knowledge graph (KG) is represented as a graph structure called  $\mathcal{G} = \{\mathcal{E}, \mathcal{R}, \mathcal{F}\}$ , where  $\mathcal{E}, \mathcal{R}$ , and  $\mathcal{F}$  denote the sets of entities, relations, and facts, respectively. Specifically, the fact set  $\mathcal{F} = (h, r, t) \mid h, t \in \mathcal{E}, r \in \mathcal{R}$  stores the triples in the KG. Each triple  $(h, r, t) \in \mathcal{F}$  represents a directed edge from entity h to entity t with a relation type r.

Definition 2.2 (Knowledge Graph Construction). KG construction is facilitated through the utilization of a procedure denoted as f, which maps a data source to a KG:  $f:D\times f_k(D)\to \mathcal{G}$ . In this context, D represents the set of data sources, and  $f_k(D)$  corresponds to the background knowledge associated with the data target, which can include domain-specific knowledge. It is crucial to highlight that the availability of background knowledge plays a significant role in KG construction. This background knowledge can be provided through pre-designed rules or a language model that generates representations. Without such background knowledge, the process of KG construction is often hindered or unable to proceed.

2 PRELIMINARY & REQUIREMENTS
2.1 Preliminary


Document: flood kg
Section: 2 PRELIMINARY & REQUIREMENTS > 2.2 Requirements
Type: text

To guide our system design for a comprehensive and reproducible platform, we summarize the requirements as following three as-pects:

- Data and storage compatibility. When integrating data of different types, formats, and sources into the same storage system, the integrity, consistency, and accessibility of the data can be maintained.
- Algorithm universality. Knowledge representation algorithms can be applied universally to downstream subtasks.
- Knowledge maintainability. The system must effectively manage and update urban flood event data to ensure its accuracy and relevance.

Therefore, according to the above definitions and requirements, we build the UrbanFloodKG system, which is introduced in the following.

2 PRELIMINARY & REQUIREMENTS
2.2 Requirements


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM
Type: text

In this section, we present an overview of our designed Urban-FloodKG system. Subsequently, we delve into the specific details from a layered perspective.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview
Type: text

The high-level system architecture of the UrbanFloodKG system is shown in Fig. 1. The different layers are described as follows:

- Data. The data layer is responsible for collecting data from multiple sources and cleaning the collected data.
- Graph. This graph layer constructs UrbanFloodKG by defining its schema, extracting entities and relationships from urban data of different structures and forms, and enriching them with additional attributes. By integrating urban flood data in this way, the construct UrbanFloodKG provides a comprehensive and effective platform for urban flood risk

3 THE URBAN FLOOD KG SYSTEM
3.1 System Overview


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview
Type: figure
Figure

Image /page/2/Figure/2 description: A diagram illustrating the high-level architecture of the UrbanFloodKG system, organized into four distinct layers: Application, Algorithm, Graph, and Data. The bottom layer, Data, involves 'Data Collection & Cleaning' of 'Structured data', 'Semi-structured data', and 'Unstructured data'. The next layer up is Graph, which is divided into 'Knowledge Extraction' (including Schema, Entity, Relation, and Attribute) and 'Knowledge Storage' (using OrientDB). Above that is the Algorithm layer, which has two parts: 'Knowledge Representation' (using 'Translation-based models & GNN-based models') and 'Operation' (including Query, Embedding, Link Prediction, and Node Classification). The top layer is Application, which handles Tasks such as 'Knowledge Reasoning', 'Event Classification', 'Urban Decision-making', and others indicated by an ellipsis.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview
Type: figure
Figure: Figure 1: The high-level architecture of UrbanFloodKG system.

Figure 1: The high-level architecture of UrbanFloodKG system.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview
Type: text

assessment. Next, all constructed triplets are transformed into graph data structures and input into OrientDB [36].

- Algorithm. The algorithm layer utilizes Translation-based models and GNN-based models to transform the triplets in the KG into vector embeddings, while also offering fundamental operations.
- Application. In the application layer, the application scenarios of this system are introduced. This layer provides services for knowledge reasoning, event classification, and urban decision-making tasks.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer
Type: text

The data layer provides data collection and data cleaning functions.

*Data Collection.* The data types of urban flood events can be divided into three categories: structured data, semi-structured data, and unstructured data, as shown in Fig. 2.

- **Structured data.** Most structured data are mainly tables, and we extract key information manually.
- Semi-structured data. Most semi-structured data are texts. Named entity recognition [19, 26] and relation extraction tasks are of great help in extracting knowledge from unstructured text. We use the UIE [24] framework to extract

- knowledge from text automatically, extract key attributes as event attribute information.
- Unstructured data. The unstructured data mainly consist of manually collected images of water-logging scenes.

3 THE URBAN FLOOD KG SYSTEM
3.2 Data Layer


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer
Type: figure
Figure

Image /page/2/Picture/14 description: A diagram illustrating three types of information, labeled "Table," "Image," and "Text," arranged horizontally. On the left, under the label "Table," is a black and white icon of a grid with four columns and three rows. In the center, under the label "Image," is a photograph of a flooded urban street with reflections of buildings in the water and a car in the distance. On the right, under the label "Text," is a text box containing the sentence: "The Shinan Avenue experienced urbanflood, lasting for one hour."


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer
Type: figure
Figure: Figure 2: Multi-modal data related to urban flood events.

Figure 2: Multi-modal data related to urban flood events.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer
Type: text

The graph layer is responsible for the management of KG data. In the knowledge extraction stage, we summarize the following four key aspects: schema definition, entity identification, relation extraction and attribute enrichment.

*Schema Definition.* To ensure entities in the KG have a consistent structure and semantics, thereby enhancing the reliability of the KG, a schema or ontology is used to describe the high-level

structure of the KG. This includes defining the types of entities and relations present within the graph [15]. Fig. 3 illustrates the schema of UrbanFloodKG, wherein the rectangles represent various entity types and the edges depict their relationships within the UrbanFloodKG.

3 THE URBAN FLOOD KG SYSTEM
3.3 Graph Layer


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer
Type: figure
Figure

Image /page/3/Figure/3 description: A concept map or entity-relationship diagram showing the relationships between various entities. The entities are represented by light blue rounded rectangles, and the relationships are shown as labeled arrows. The diagram shows that an 'Image' is an 'on-siteImageOf' a 'Waterlogging Area'. The 'Waterlogging Area' is 'locateAt' a 'Region', and a 'Department' is also 'locateAt' a 'Region'. A 'Reason' can 'leadTo' an 'Event', which can 'occurIn' a 'Waterlogging Area'. An 'Event' can 'leadTo' an 'Influence' and is a 'cateOf' a 'Risk-level'. There is a recursive relationship on 'Event' labeled 'samePosition, closeTime'. A 'Leader' can 'resolve' an 'Event', and the 'Leader' 'belongTo' a 'Department'.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer
Type: figure
Figure: Figure 3: The schema of UrbanFloodKG. Each rectangle represents a schema of an entity.

Figure 3: The schema of UrbanFloodKG. Each rectangle represents a schema of an entity.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer
Type: text

**Entity Identification.** To ensure the standardization and consistency of the content in the KG, it is necessary to establish conventions for its representation. Therefore, the following entities can be identified:

- Event. Event represents each specific urban flood event, which is a key entity in the research of UrbanFloodKG.
- Influence. Influence represents the impact caused by urban flood events.
- Reason. Reason represents the causes of urban flood events summarized by professionals.
- Leader. Leader represents the personnel responsible for handling urban flood events.
- Department. Department represents the department to which personnel responsible for handling urban flood events belong.
- Region. The Region denotes the administrative area of the city to which the urban flood event is attributed.
- Water-logging Area. Water-logging Area represents the specific location where the urban flood events occurred.
- Image. Image stores on-site photographs depicting the urban flood events.
- **Risk-level**. Risk-level represents the the assessment of the risk level of urban flood events conducted by experts during the later stages.

**Relation Extraction.** After analyzing the identified entity types within UrbanFloodKG, we extract representative relations to capture the semantic connections between entities. These relations are categorized as follows:

• **Spatial Relations.** The spatial relationship model characterizes the spatial association between entities. The relationships "locatAt" and "occurIn" simulate the spatial connections between event entities and location entities, as well as region entities, respectively.

- Personal Relations. Personal relationships emphasize individual knowledge. The "belongTo" relationship links the department entity to the leader's workplace, while the "resolve" relationship connects the leader entity to the event entity that they are responsible for handling.
- Causal Relations. Causal relationships aid in comprehending and predicting causal connections among entities. The relationship "leadTo" establishes a link between the event entity and the reason entity or the influence entity, forming a comprehensive chain of events.
- Affiliated Relations. The association relationship signifies
  the linkage between entities. "on-siteImageOf" connects the
  on-site image entity with the water-logging area entity, while
  "cateOf" establishes a connection between the event entity
  and the risk-level entity. "samePosition" and "closeTime" link
  event entities with the same location or close occurrence
  time, effectively capturing the association between entities.

Attribute Enrichment. To increase the amount of information and expressiveness of entities in the KG. By adding more attributes to entities, a more detailed and comprehensive description can be provided, making the KG richer and more useful. Therefore the system further enriches the entities with attribute provided, which are described as follows:

Event Attributes. The event attributes are displayed in Table 1

Attribute
 Classified Group

StartTime (ms)
 timestamp

Depth (m)
 0-0.3,0.3-0.5,0.5-0.8,>0.8

Duration (h)
 0-1,1-3,3-5,>5

Lanes (number)

Table 1: Event Attributes.

• Influence Attributes. An event's influence is described through relevant text, which can be extracted as feature maps using the Word2Vec[27] model, allowing identification of the important features of the impact text for better understanding of the event's influences.

0-3,3-6,6-9,>9


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer
Type: text

m ) 0 - 0 . 3 , 0 . 3 - 0 . 5 , 0 . 5 - 0 . 8 , > 0 . 8 Duration ( h ) 0 - 1 , 1 - 3 , 3 - 5 , > 5 Lanes ( number ) Table 1 : Event Attributes . • Influence Attributes . An event ' s influence is described through relevant text , which can be extracted as feature maps using the Word2Vec [ 27 ] model , allowing identification of the important features of the impact text for better understanding of the event ' s influences . 0 - 3 , 3 - 6 , 6 - 9 , > 9

- Reason Attributes. The cause of an event is also conveyed through text, with its characteristic attribute being the relevant textual description of what triggered the event. This text-based information can also be extracted as feature maps using the Word2Vec model, allowing for a better understanding of the cause of the event.
- Leader Attributes. The leader attribute includes the name and, contact phone number.
- Department Attributes. The department attribute includes the name and location information.
- Region Attributes. The region attribute includes the boundary, area, and number of buildings of the region.
- Water-logging Area Attributes. The attribute of the water-logging area comprises specific location information and area information pertaining to the water-logging area.

- Image Attributes. The on-site image attribute of the event is extracted as feature maps using the ResNet[14] model.
- Risk-level Attributes. The event's risk-level attribute comprises three categories: mild, moderate, and severe.

Knowledge Storage. Currently, there are several popular graph databases available, including Neo4j [47], JanusGraph [29], Arango-DB [53], and TigerGraph [8]. It is worth mentioning that we have discovered that OrientDB [36], as a multi-model database, is also capable of effectively storing graph data. OrientDB is a full-function, NoSQL MMDMS, addressing the big data variety problem with one single, multi-model store and a SQL-based, multi-model query language.

The UrbanFloodKG system uses an object-oriented approach to store triples as node and edge classes. It employs the OrientDB database, which supports graph-based processing, SQL queries, and parallel query execution. OrientDB is not only a graph database but also a multi-model database that can handle both graphs and documents simultaneously. Table 2 shows three types of node and two types of edge in OrientDB and Fig. 4 shows a portion of the KG


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer
Type: table
Table: Table 2: In OrientDB, there are three categories of node classes - Schema Node, Entity Node and Attribute Node. Additionally, there are two types of edge classes - Schema Edge and Entity Edge. Each node and edge possesses its unique "Name" attribute.

| Class      | Type                    | Name                             |
|------------|-------------------------|----------------------------------|
| Schema     | Schema Node             | Schema name                      |
| Entity     | Entity Node             | SchemaName_Rid                   |
| Attribute  | Attribute Node          | Attribute value                  |
| SchemaEdge | Schema Relation Edge | hasInstance, Relation name    |
| EntityEdge | Entity Relation Edge | Attribute name, Relation name |


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
Type: text

*Translation-based models*. The urban flood event information is stored in OrientDB in the form of nodes and edges. It is difficult to directly analyze them, and existing research has proposed KG representation learning [11, 45]. It learns low-dimensional continuous representation vectors (also called embeddings) of entities and relationships while preserving the inherent structure and semantics of the KG.

The process of KG representation learning is described below. Given a knowledge graph  $\mathcal{G}=(\mathcal{E},\mathcal{R},\mathcal{F})$ , which includes embedding vectors  $\mathcal{E}$  and  $\mathcal{R}$  representing entities and relationships, along with a set of triples  $\mathcal{F}$ , the KG representation learning algorithm employs various scoring functions  $\phi$  to calculate scores for entity and relationship embeddings. The goal is to assign higher scores to valid triples compared to invalid ones. By utilizing a predefined loss

function  $\mathcal{L}$ , such as cross-entropy loss or hinge loss [37], the KG representation learning algorithm updates the embedding parameters iteratively until convergence is achieved.

Typical scoring functions utilized for KG representation encompass translation-based models [2]. The system incorporates two translation-based models, namely TransE [2] and HolE [32]. However, to address concerns regarding scalability, two decomposition-based models, namely ComplEx [43] and DisMult [52], are also supported.

3 THE URBAN FLOOD KG SYSTEM
3.4 Algorithm Layer


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
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Figure

Image /page/4/Figure/13 description: A diagram illustrates a neural network architecture for graph processing, flowing from left to right. The process begins with an 'Input' block containing a graph 'G' with four green nodes. This input is fed into a block labeled 'GEN', which contains four variations of the input graph. The output of the 'GEN' block passes through a 'ReLU' activation function. This is followed by a second 'GEN' block and another 'ReLU' function. The next stage is a fully connected layer labeled 'FC', depicted with four input nodes and four output nodes, all interconnected. Finally, the 'Output' block shows the resulting graph where the nodes are colored differently (orange, yellow, and green) and labeled C1, C2, and C3, suggesting a node classification or clustering task.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
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Figure: Figure 5: The GEN Network model for urban flood event classification generates a corresponding classification (C1, C2, C3) for each node.

Figure 5: The GEN Network model for urban flood event classification generates a corresponding classification (C1, C2, C3) for each node.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
Type: text

*GNN-based models*. Some graph neural network models can also be used for knowledge representation, the network structure used in our system is based on the GENeralized Graph Convolution Network (GEN) [21]. In the forward propagation process of the GEN network, the feature vector update equation for each urban flood event node is:

$$\mathbf{x}_{i}' = MLP(\mathbf{x}_{i} + AGG(ReLU(\mathbf{x}_{j} + \mathbf{e}_{ji}) + \epsilon : j \in \mathcal{N}(i)))$$
(1)

The mechanism of information collation and updating in the GEN network is delineated by Equation (1). This procedure involves the renewal of a node's feature vector via a multi-layer perceptron (MLP) and an aggregation function (AGG). In this context,  $\mathbf{x}_i$  signifies the feature vector of node i, while  $\mathbf{x}_j$  is a symbol for the feature vector of node j. Additionally,  $\mathbf{e}_{ji}$  denotes the edge feature vector, encapsulating the attribute information of the edge that connects node j to node i. Besides,  $\epsilon$  is a learnable parameter vector employed to modify the scale and shift of the node feature vector. The model's AGG function makes use of a summation aggregation function.

The structure of the event classification model is shown in Fig. 5. The urban flood event classification model defines two layers of GEN networks and a fully connected layer. The input is a graph G, which includes the feature vectors of each node (node features), the edge indices of adjacent nodes (edge indices), and the feature vectors of each edge (edge attributes). The forward propagation process of the GEN model involves propagating and integrating information on graph-structured data. Nodes update their feature vectors based on information from neighboring nodes, capturing both local and global information within the graph structure.

*Operations*. Typical functions in traditional graph systems [50] are supported by the UrbanFloodKG system, such as Cypher and embedding access. In addition, in order to adapt the system to urban flood risk assessment applications, we have developed three types of functions, which are abstracted as basic operations in Table 3.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
Type: figure
Figure

Image /page/5/Figure/2 description: A diagram illustrates a knowledge graph with a schema level and an entity level. A legend explains the components: white circles are Schema Nodes, light blue are Entity Nodes, light green are Attribute Nodes, black arrows are Schema Relation Edges, and blue arrows are Entity Relation Edges. The schema level shows nodes for 'Event', 'Department', and 'Leader'. The relationships are: 'Leader' resolves 'Event', and 'Event' belongs to 'Department'. The entity level shows instances: 'Departmnet\_#32:1' is an instance of 'Department' with the name 'Drainage Company'. 'Event\_#30:1' is an instance of 'Event' with a duration of '0-1' and a startTime of '1621900800'. 'Leader\_#31:1' is an instance of 'Leader' with the name 'Zhang' and phone '1391111111'. An entity relation edge connects 'Event\_#30:1' to 'Leader\_#31:1'.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
Type: figure
Figure: Figure 4: The portion of UrbanFloodKG. Each Entity Node is linked to multiple Attribute Nodes via an Entity Relation Edge, where the edge name corresponds to the attribute name.

Figure 4: The portion of UrbanFloodKG. Each Entity Node is linked to multiple Attribute Nodes via an Entity Relation Edge, where the edge name corresponds to the attribute name.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
Type: table
Table: Table 3: Operations.

| Operation                               | Return             | Description                                              |
|-----------------------------------------|--------------------|----------------------------------------------------------|
| query(Sql_Command)                      | return result      | Executes Cypher query based on OrientDB on UrbanFloodKG. |
| get_emb(entities, embedding_type,model) | return embedding   | Get the embeddings of entities or relations.             |
| node_cla(feature_Vector,model)          | return level       | Return risk level of event.                              |
| link_pred(src_ent, tar_ent, rel, model) | return probability | Calculates plausibility of an input triple.              |


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer
Type: text

We use the interfaces provided by ampligraph [7] and PyG [12] to implement the latter three proposed operations.

 query. This operation accepts the Cypher query from the user, which returns the corresponding results on the Urban-FloodKG. For example, users can obtain information about urban flood events that occurred on Shi Nan Road via below query command.

```
MATCH {class:Entity,
WHERE:(name='ShiNanRoad')}.
inE('EntityEdge'){as:m,
WHERE:(name='occurIn')}
RETURN m.out
```

• get\_emb. This operation offers a direct interface for accessing the embeddings of entities or relations in UrbanFloodKG. It particularly provides embeddings for models depending on the input model.

- node\_cla. This operation accomplishes the node classification task within the KG. By supplying the entity's vector feature, the UrbanFloodKG system invokes this operation to perform classification using the embeddings learned by a GNN-based model.
- link\_pred. This operation enables relational link prediction between two entities, src\_ent and tar\_ent, by calculating a score using the input model. And users can obtain the likelihood of a triple fact.


Document: flood kg
Section: 3 THE URBAN FLOOD KG SYSTEM > 3.5 Application Layer
Type: text

The proposed framework in this paper can be applied to various aspects of urban flood control, which can be summarized as follows:

**Knowledge reasoning**. Based on the KG representation learning model, the possibility of a triplet can be predicted. After training with a large amount of data, users can enter a triplet by themselves,

File Name **Basic Sample Format** Records Event\_ID | StartTime | Duration | Lanes | Depth | Area\_ID | Influence\_ID event.txt 10,000 | RiskLevel 1|1621900800 | 0-3| 1-3 | 0.3-0.5| 4 | 2 | mild Area\_ID | Name 422 waterlogging-area.txt 1|Shinan Avenue Reason\_ID | Name |Event\_ID reason.txt 873 1|large drainage area|1 Influence\_ID | Name influence txt 653 1|Power and communication system interruption Event\_ID | Event\_ID same\_position.txt 2155 1|2 Event ID | Event ID close\_time.txt 1415 1|2Image\_ID | Embedding |Area\_ID image.txt 10000 1 | [0.164,0.135,0.223,...] | 1

Table 4: The files of UrbanFlood Dataset. Each entity has its own attributes and associated ID values with other related entities.

for example (?, leadTo, a certain event), users can input some possible causes of urban flood in the ?, and the model will return a possibility for reasoning the cause of urban flood for a certain event.

*Event classification.* Users can enter various information about urban flood events, such as rainfall, depth, duration, etc. The model predicts the risk level of the event, assigning it a classification grade includes: mild, moderate, or severe. Based on the input information, the model analyzes the event's characteristics, conducts quantitative analysis, calculates the risk score, and maps it to the corresponding risk level.

*Urban decision making*. The model accurately assesses and predicts the risks of urban flood events, aiding city decision-makers in understanding the risk characteristics and impact scope of such events and taking more effective countermeasures. KG-based risk assessment and prediction of urban flood events provides important data and knowledge support for city decision making, contributing to the sustainable development and safe operation of cities.

3 THE URBAN FLOOD KG SYSTEM
3.5 Application Layer


Document: flood kg
Section: 4 EXPERIMENT
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In this section, firstly, the construction of the urban flood event dataset is introduced. Then, combined with specific cases, model comparison experiments of link prediction and node classification tasks are carried out to evaluate the effectiveness and applicability of the UrbanFloodKG system we designed.


Document: flood kg
Section: 4 EXPERIMENT > 4.1 DataSet Construction & Environment
Type: text

We gather 10,000 urban flood event data from 2010 to 2018 in Guangzhou and make the dataset. The files included in Table 4 provide details about the dataset. Based on this dataset, we conduct subsequent experiments of link prediction and node classification. The experiments are conducted using PyTorch 2.0.0 on a

host equipped with an AMD Ryzen7 5800 CPU, 32GB RAM, and one NVIDIA GeForce RTX 4080 GPU.

4 EXPERIMENT
4.1 DataSet Construction & Environment


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: text

In this part, we explore representative applications in the urban flood scenario and summarize the challenges in urban flood research that can be regarded as link prediction problems.

For performance comparison, we adopt the commonly used metrics in the respective task. By conducting performance comparison experiments with various models, we demonstrate the effectiveness of the UrbanFloodKG system.

**Causal Prediction.** The causal prediction use case formulates the traditional causal prediction problem [31] into the link prediction problem on the UrbanFloodKG, which is stated as follows:

Problem 1. UrbanFloodKG-based Reason Prediction Problem. Given the UrbanFloodKG  $\mathcal{G} = \{\mathcal{E}, \mathcal{R}, \mathcal{F}\}$ , a recorded information of urban flood event like reason  $e_r$  lead to the event  $e_e$  can be expressed as  $(e_r, r_{leadTo}, e_e)$  with  $e_r$ , and  $e_e$  as entities and  $r_{leadTo}$  as relation therein.

Hence, the reason prediction problem of potential reasons of an urban flood event  $e_e$ , can be formulated as the link prediction problem of  $(?, r_{leadTo}, e_e)$  in UrbanFloodKG.

The overall framework is depicted in Fig. 6, where the application layer utilizes the link\_pred operation to predict if there exist leadTo links between reason entities and event entities.

To evaluate the proposed framework, we extract a subset of data from our self-constructed UrbanFlood dataset. The dataset is split into train/validation/test sets, following a ratio of 7:1:2.

By using four KG representation learning models: TransE, ComplEx, DistMult, and HolE, we evaluate the effectiveness of our system for reason prediction using the MRR, MR, and Hits@n metrics. Table 5 presents results and illustrates the successful performance of our system in reason prediction.

4 EXPERIMENT
4.2 Link Prediction Analysis


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: figure
Figure

Image /page/7/Figure/2 description: A flowchart illustrating a data processing pipeline. An initial block labeled "UrbanFlood System" has two arrows pointing to two separate databases, represented by cylinders. The top database is labeled "Reason" and the bottom one is labeled "Event". From the "Reason" database, an arrow labeled "get\_emb" points to a parallelogram labeled "Reason embedding". Similarly, from the "Event" database, an arrow labeled "get\_emb" points to a parallelogram labeled "Event embedding". Both the "Reason embedding" and "Event embedding" blocks have arrows that converge into a single line. This line is labeled "link\_pred" and points to a final rounded rectangle labeled "leadTo".


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: figure
Figure: Figure 6: The illustration of leveraging UrbanFloodKG system for reason prediction problem.

Figure 6: The illustration of leveraging UrbanFloodKG system for reason prediction problem.


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: table
Table: Table 5: The result comparison of reason prediction task.

| Model    | MRR  | MR      | Hits@10 | Hits@3 | Hits@1 |
|----------|------|---------|---------|--------|--------|
| ComplEx  | 0.36 | 1166.96 | 0.69    | 0.54   | 0.16   |
| DistMult | 0.39 | 1010.31 | 0.72    | 0.56   | 0.19   |
| HolE     | 0.49 | 884.47  | 0.85    | 0.71   | 0.27   |
| TransE   | 0.38 | 457.92  | 0.66    | 0.49   | 0.13   |


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: text

Problem 2. UrbanFloodKG-based Influence Prediction Problem. Given the UrbanFloodKG  $\mathcal{G} = \{\mathcal{E}, \mathcal{R}, \mathcal{F}\}$ , a recorded information of urban flood event like event  $e_e$  led to the influence  $e_i$ , can be expressed as  $(e_e, r_{leadTo}, e_i)$  with  $e_e$ , and  $e_i$  as entities and  $r_{leadTo}$  as relation therein.

Hence, the influence prediction problem of potential influences caused by an urban flood event  $e_e$ , can be formulated as the link prediction problem of  $(e_e, r_{leadTo}, ?)$  in UrbanFloodKG.

The overall framework is illustrated in Fig. 7. Especially, the application layer calls the operation link\_pred to predict if there exist leadTo links between event entities and influence entities.


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: figure
Figure

Image /page/7/Figure/9 description: A flowchart illustrating a system for predicting relationships. The process starts with an "UrbanFlood System" box, which branches into two parallel paths. The top path leads to a database labeled "Event," which is then processed by a "get\_emb" function to create an "Event embedding." The bottom path leads to a database labeled "Influence," which is also processed by a "get\_emb" function to create an "Influence embedding." Both the "Event embedding" and "Influence embedding" are then combined and fed into a function labeled "link\_pred," which results in a final output labeled "leadTo."


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: figure
Figure: Figure 7: The illustration of leveraging UrbanFloodKG system for influence prediction problem.

Figure 7: The illustration of leveraging UrbanFloodKG system for influence prediction problem.


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: text

To evaluate the proposed framework, we additionally extract a subset of data from our self-constructed UrbanFlood dataset. The dataset is split into train/validation/test sets, following a ratio of 7:1:2. We evaluate the effectiveness of our system for influence prediction by employing four KG representation learning models. Table 6 presents results and illustrates the successful performance of our system in influence prediction.


Document: flood kg
Section: 4 EXPERIMENT > 4.2 Link Prediction Analysis
Type: table
Table: Table 6: The result comparison of influence prediction task.

| Model    | MRR  | MR     | Hits@10 | Hits@3 | Hits@1 |
|----------|------|--------|---------|--------|--------|
| ComplEx  | 0.55 | 158.83 | 0.67    | 0.60   | 0.49   |
| DistMult | 0.63 | 135.06 | 0.74    | 0.67   | 0.56   |
| HolE     | 0.70 | 165.20 | 0.82    | 0.75   | 0.64   |
| TransE   | 0.43 | 62.15  | 0.57    | 0.47   | 0.35   |


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: text

In this part, we summary the issues about risk assessment in ur-banFlood research that can be considered as node classification problems.

**Risk-level classification.** For the risk assessment of urban flood events, we believe that two hypotheses can be proposed:

- The risk of individual events increases as the number of urban flood events linked to time and location rises. For example, when multiple urban flood events transpire in a particular area within a short period, it suggests that the area possesses inadequate flood prevention measures, thereby escalating the risk of subsequent events.
- The risk increases with the degree of the event node. For instance, when an urban flood event is connected to numerous surrounding urban flood entities, it suggests that the event is located in a high-risk event aggregation area and consequently carries a higher level of risk itself.

In summary, the risk assessment of events should consider both time and spatial elements, not just the events themselves. In our constructed UrbanFlood dataset, the expert evaluation level of the risk of each event, represented by the Risk-level entity, has already been included. Starting from the events' own attributes and considering the related events, we can discover the classification rules between events and effectively assess future urban flood events.

The overall framework is illustrated in Fig. 8. Especially, the application layer calls the operation node\_cla to classify each event node.

4 EXPERIMENT
4.3 Node Classification Analysis


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: figure
Figure

Image /page/7/Figure/21 description: A block diagram illustrates a data processing workflow. The process starts with a rectangle labeled "UrbanFlood System" which sends a "query" to a database labeled "Event-related entity". Data from this database and another database labeled "Event" are then processed by a "Feature Engineering" block. The output is a parallelogram labeled "Feature Vector". This vector is then used in a process labeled "node\_cla" to produce the final output, which is a rounded rectangle labeled "Risk-level".


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: figure
Figure: Figure 8: The illustration of leveraging UrbanFloodKG system for Risk-level classification problem.

Figure 8: The illustration of leveraging UrbanFloodKG system for Risk-level classification problem.


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: text

To evaluate the proposed framework, we sample a subset of data from our self-constructed UrbanFlood dataset. The basic statistics of the dataset are summarized in Table 7. The dataset is split into train/validation/test sets, following a ratio of 7:1:2.


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: table
Table: Table 7: Statistics of UrbanFlood dataset for risk-level classification.

| #Nodes | #Edges | #Attrs | Train | Valid | Test |
|--------|--------|--------|-------|-------|------|
| 10,000 | 3570   | 6      | 7000  | 1000  | 2000 |


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: text

The experiment compares multiple models and evaluates them using accuracy, precision, recall, and F1-score metrics. The experimental results are shown in Table 8.


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: table
Table: Table 8: The result comparison of risk-level classification prediction task.

| Model     | Accuracy | Precision | Recall | F1-score |
|-----------|----------|-----------|--------|----------|
| GATv2 [4] | 0.3564   | 0.5883    | 0.3564 | 0.2669   |
| SG [48]   | 0.3832   | 0.7636    | 0.3832 | 0.2123   |
| AGNN [42] | 0.6741   | 0.6640    | 0.6741 | 0.6558   |
| TAG [10]  | 0.3815   | 0.7640    | 0.3815 | 0.2107   |
| GIN [51]  | 0.3914   | 0.7618    | 0.3914 | 0.2202   |
| GEN       | 0.9136   | 0.9129    | 0.9136 | 0.9126   |

Model Accuracy Precision Recall F1-score


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: text

The classification network model based on the GEN model achieves an accuracy of 91%, surpassing other graph neural network models. Additionally, it reveals that by considering the features and properties of urban flood event nodes, it is possible to represent nodes of different urban flood events as vectors for accurate risk assessment.

The classification results are visualized in Fig. 9. After the multidimensional feature vectors of each urban flood event node are reduced by PCA, they are divided into three categories based on the predicted level: mild, moderate, and severe. This approach can effectively assess and predict the risk level of urban flood events.


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: figure
Figure

Image /page/8/Figure/9 description: A scatter plot with an x-axis ranging from -15 to 15 and a y-axis ranging from -10 to approximately 12. The plot displays three categories of data points, indicated by a legend in the top right corner. Blue dots represent 'mild', orange dots represent 'moderate', and green dots represent 'severe'. The green 'severe' points are clustered on the left side of the plot, primarily for x-values less than 0. The orange 'moderate' points form a vertical band in the center of the plot. The blue 'mild' points are clustered on the right side of the plot, primarily for x-values greater than 5.


Document: flood kg
Section: 4 EXPERIMENT > 4.3 Node Classification Analysis
Type: figure
Figure: Figure 9: The Visualization of the Classification Results.

Figure 9: The Visualization of the Classification Results.