Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

Flash floods are the result of climatic and hydrological extremes and are manifested by dynamic and complex processes of movement of water and sediment. They represent the most frequent and widespread natural disaster at the global level, with unwanted ecological and economic consequences. The main causes of flash floods are related to numerous meteorological and physical–geographical factors. In the territory of Serbia, flash floods represent the most common natural risk with serious consequences for people’s lives and activities. Flash flood susceptibility mapping plays a crucial role in flood risk assessment and management. The current study prepared a flood inventory using light detection and ranging (LiDAR) derived digital elevation model, and it used integrated tree machine learning models (random forest [RF], classification and regression trees [CART], and support vector machine [SVM]) to predict flood susceptibility in the Ljuboviđa watershed, municipality Ljubovija, western Serbia. First, 12 independent variables were employed as conditioning factors: lithology, rainfall, land use/cover, elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, and normalized vegetation index. Using the well-known scikit-learn (train_test_split) Python module, the flood inventory dataset was split into 70 and 30% for training and validation, respectively. The models’ performance was additionally assessed using the area under the curve (AUC). The results of the accuracy assessment demonstrated that the models for predicting flood susceptibility, RF, CART, and SVM, had AUC values of 0.854, 0.802, and 0.831, respectively; it means that RF had 85.4%, CART 80.2%, and SVM 83.1% chance of correctly ranking a random positive example higher than a random negative example, which represents the predictive power of the used models. When it came to predicting flood susceptibility, the RF model outperformed the other models used. This model estimates that 15.49, 16.04, 15.67, 23.10, and 29.70% of the watershed are very low, low, moderate, high, and extremely highly susceptible to floods, respectively. Thus, our study shows that data produced from LiDAR is potentially helpful in managing flood risk, particularly when assessing flood-related issues in the future. Flash-flood susceptibility maps have become a vital tool for risk prevention and management for government and local authorities (particularly national and local civil protection agencies, urban planning and land management departments, Ministries of Water and Environment), emergency response services (police, fire, and medical services), infrastructure and utilities sectors, insurance companies, and others.

Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.

In this study, artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) methods were used to prepare the flood susceptibility map. For this purpose, the parameters of ten, slope, land curvature, topographic moisture index, distance from river and geology and type of lands in Haraz watershed in Mazandaran province were performed. Eleven conditioning factors including slope, land curvature, distance to river, river density, elevation, rainfall, stream power index (SPI), topographic wetness index (TWI), lithology, land use and normalized difference vegetation index (NDVI) were used in Haraz watershed in Mazandaran province. In addition, 201 floodplains were located in the area. The points were randomly divided into groups of 141 points (70%) and 60 points (30%) for training and validation, respectively. Furthermore, the probability of flooding for each class of each factor was calculated. Hence, the weights obtained for each class in the Geographic Information System (GIS) were applied in the respective layers, and the flood susceptibility maps of the study area were obtained. Based on the flood susceptibility map, the area was divided into 5 classes with very high, high, medium, low and very low sensitivity. These methods were evaluated by area under the curve (AUC) method. The results indicate that the lower and near elevation to river have a high probability and sensitivity to flooding. The results of the current study showed that the frequency ratio (AUC = 0.97) and evidential belief function (AUC = 0.94) and artificial neural network (AUC = 0.87) methods had the highest accuracy in predicting flood occurrence, respectively. The results suggest that these models can be useful and reliable in predicting flood risk potential, especially in different areas, including urban spaces, due to their high efficiency. Extended Abstract Introduction To prevent, control and control floods and Prevention of possible damages, areas with high flood potential first should be considered and foremost identified and then Identify the factors that produce and create floods. In this regard, the level of flood-prone and flood-prone areas in the country has increased and Many cities, villages, industrial and agricultural facilities and residential areas They are at risk of flooding. In the event of a flood There are many factors involved. Generally Climatic factors, regional factors and human factors play a role in creating floods. Climatic factors can be He pointed to Dry area, heavy rainfall and relatively short continuity. One of the most important factors in the field can be mentioned Geological condition, vegetation, basin area, basin shape and form, basin slope and focal point. Also human intervention in the natural water cycle via Destruction of vegetation in watersheds, Irregular land use, Development of impenetrable levels and the like Increased the likelihood of flooding in various areas. In Sail management, some of these factors are controllable and ‌In design, flood control They need more attention. Due to the increasing trend of floods in the country and the growing negative effects of its occurrence in the northern parts of the country, its necessary to reduce the risk of loss of life, property and environmental risk, Necessary measures should be considered. among the various watersheds in the north of the country, in this study, Haraz watershed has been selected as the study area That The reason for choosing it on the one hand It is located and adjacent to key cities in the north of the country, including The cities of Amol, Mahmoud Abad, Babol, Babolsar, Ghaemshahr, Sari, Pol-e Sefid, Shirgah, Neka, Behshahr, Galugah and Bandar-e-Gaz and also Hundreds of rural points and thousands of hectares of agricultural and garden lands and Part of the road along the Caspian Sea (Rasht to Gorgan) and Parts of the mountainous roads of Amol to Tehran and Ghaemshahr to Tehran in this basin and on the other hand There has been a growing flood in recent years in this geographical area that Numerous social, economic and environmental damages and challenges. So these are the reasons The preparation of a flood susceptibility map in the Haraz watershed makes it even more necessary. according to the above and Description of flood hazards in the northern regions of the country, the questions in this study are: What are the most dangerous parts of Haraz watershed in terms of flood sensitivity? Efficiency of which of the artificial neural network models, Frequency ratio and Is the function of definitive evidence more to prepare a flood susceptibility map in Haraz watershed? Methodology Current research in terms of purpose Is a type of applied research and done by quantitative method. According to the objectives of the research, the required data Has been collected from the relevant organizations and organs (Regional Water Company, Natural Resources Department, etc.) and to analyze this data Used the ArcGIS software. Overall, the research process is as follows First Prepared List of past floods in the study area and so on has been identified Effective parameters in flood occurrence and using Three models of definite evidence function (EBF), frequency ratio (FR) and artificial neural network (ANN), A flood sensitization map of Haraz watershed has been prepared. The following is a review Model Validation Using the ROC curve. Results and discussion The weights obtained in each method, for each class of each factor Applied in Geographic Information System (GIS) and Flood susceptibility maps were prepared for Haraz watershed. Flood susceptibility maps Launched in ArcGIS10.3 software environment in five classes, the sensitivity is very low, low, medium, high and very high. In order to assess the accuracy of the flood prediction map, 60 flood events were used (Experimental data) Related to previous courses and These events have not been entered to predict flood potential in probabilistic models. Given that the area below the curve for the model, the frequency ratio is 0.97 So this model is more efficient Definitive Evidence for Model Function Models (0.93) and The neural network is artificial (0.78). Conclusion The present study is done with the aim of preparing a map of the possibility of floods in the watershed of Haraz and Evaluate the efficiency of frequency-ratio models, the function of definitive evidence, and the artificial neural network in the preparation of flood susceptibility maps. To do this, 201 flood points were recorded and 141 Flood situation for modeling and 60 positions were set aside for model validation. To prepare these maps, the first step is to prepare the factors that affect the occurrence of floods. The findings of this study indicate the accuracy of the probability frequency ratio model in identifying areas with flood susceptibility in Haraz watershed in Mazandaran province. Therefore, the use of probability frequency model It is useful and reliable in assessing the risk of flooding. But since The accuracy of predicting models of definite evidence and artificial neural networks is also acceptable. These methods can also be used, but in general, the frequency ratio has a higher accuracy in predicting flood areas. In the maps produced, Parts with low and low elevation classes Exit area, they have the highest amount of tracking. generally, Areas with low elevation and low slope, they are most likely to be flooded. The predictive results also showed that Slope parameters, height, land curvature, lithology, land type, river distance, river carrying capacity and topographic moisture index are influential on Potential flooding potential and using them is useful in probabilistic models, flood potential assessment. Flood formation mechanism and landslide flooding in the form of spatial analysis, it can be extended to other parts of the watershed. The approach presented in this research in fact, some variables affecting the occurrence of floods have been used Which are very important in the flood risk prediction map in the study area which can be used using the results of these maps, He took appropriate management measures to reduce the damage and casualties caused by the floods. To be careful in predicting flood occurrence It is necessary to use other machine learning models or a combination of these models Which will increase the accuracy of the flood prediction. The above findings, in addition to having practical and operational aspects for management devices and institutions in particular, the Crisis Management Headquarters of the northern provinces of the country, can be used as a suitable template, By researchers and those interested in flood urban crisis management planning. Prepare a hybrid susceptibility map for multiple hazards (Flood, earthquake, drought, etc.) Using hybrid models for the study area and other watersheds of the country Especially in areas with high urban population density. Recommended as a basis for future studies.

One of the most common natural disasters is flooding, which has the potential to seriously harm environments and infrastructure. Flood susceptibility mapping (FSM) is the main way to manage flood risk. It measures how likely a region is to flood in a quantitative way. The purpose of this study was to develop state-of-the-art ensemble machine learning (ML) models for flood prediction and to identify the most suitable approach for accurate flood susceptibility mapping. This study leverages diverse datasets, including elevation, slope, aspect, plan curvature, topographic wetness index, stream power index, distance from rivers, soil, rainfall, land use/land cover, and drainage density, which were used as conditioning factors to evaluate flood susceptibility in the Choke Watershed. Three machine learning (ML) algorithms were employed: Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). Model performance was assessed using confusion matrix metrics and the area under the receiver operating characteristic curve (AUROC). The Gradient Boosting (GB) and Extreme Gradient Boosting (XGBoost) models scored the highest in terms of test accuracy (0.97), followed by RF (0.96). This study is the first application of these models in the Choke Watershed for flood susceptibility mapping, with potential for broader applications to other natural disasters, including earthquakes and landslides. The results help strengthen global efforts aimed at mitigating natural disaster risks, particularly in Ethiopia, and advancing environmental sustainability.

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds.

Floods are among the most common natural disasters that impose severe financial and human losses every year. Therefore, it is necessary to prepare susceptibility and vulnerability maps for comprehensive flood management to reduce their destructive effects. This study is trying to provide a flood susceptibility mapping in Jahrom (Fars Province) using a combination of frequency ratio (FR) and adaptive neuro-fuzzy inference system (ANFIS) and compare their accuracy. Totally, 51 flood locations areas were identified, 35 locations of which were randomly selected to model flood susceptibility and the remaining 16 locations were used to validate the models. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected, and the corresponding maps were prepared using ArcGIS. After preparing the flood susceptibility maps using these methods, the relative operating characteristic (ROC) curve was used to evaluate the results. The area under the curve (AUC) obtained from the ROC curve indicated the accuracy of 89% and 91.2% for the ensembles of FR and ANFIS-FR models, respectively. These results can be useful for managers, researchers, and designers in managing flood vulnerable areas and reducing their damages.

Flooding is a very common worldwide natural hazard causing large-scale casualties every year; Iran is not immune to this thread as well. Comprehensive flood susceptibility mapping is very important to reduce losses of lives and properties. Thus, the aim of this study is to map susceptibility to flooding by different bivariate statistical methods including Shannon's entropy (SE), statistical index (SI), and weighting factor (Wf). In this regard, model performance evaluation is also carried out in Haraz Watershed, Mazandaran Province, Iran. In the first step, 211 flood locations were identified by the documentary sources and field inventories, of which 70% (151 positions) were used for flood susceptibility modeling and 30% (60 positions) for evaluation and verification of the model. In the second step, ten influential factors in flooding were chosen, namely slope angle, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, rainfall, geology, land use, and normalized difference vegetation index (NDVI). In the next step, flood susceptibility maps were prepared by these four methods in ArcGIS. As the last step, receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated for quantitative assessment of each model. The results showed that the best model to estimate the susceptibility to flooding in Haraz Watershed was SI model with the prediction and success rates of 99.71 and 98.72%, respectively, followed by Wf and SE models with the AUC values of 98.1 and 96.57% for the success rate, and 97.6 and 92.42% for the prediction rate, respectively. In the SI and Wf models, the highest and lowest important parameters were the distance from river and geology. Flood susceptibility maps are informative for managers and decision makers in Haraz Watershed in order to contemplate measures to reduce human and financial losses.

Flood is one of the most prevalent natural disasters that frequently occur in the northern part of Iran reported in hot spots of flood occurrences. The main aim of the current study was to prepare flood susceptibility maps using four models, namely frequency ratio (FR), weights-of-evidence (WofE), analytical hierarchy process (AHP), and ensemble of frequency ratio with AHP (FR-AHP), and to compare them at Haraz Watershed in Mazandaran Province, Iran. A total of 211 flood locations were prepared in GIS environment, of which 151 locations were randomly selected for modeling and the remaining 60 locations were used for validation aims. In the next step, 10 flood-conditioning factors were prepared including slope angle, plan curvature, elevation, topographic wetness index, stream power index, rainfall, distance from river, geology, landuse, and normalized difference vegetation index. The receiver operating characteristic curve and the area under the curve (AUC) were created for different flood susceptibility maps. Validation of results showed that AUC values for success rate in training data set, for FR, WofE, AHP, and FR-AHP, were 97.07, 98.96, 95.91, and 86.19 % with prediction rates of 0.9657 (96.57 %), 0.9596 (95.96 %), 0.9492 (94.92 %), and 0.8469 (84.69 %), respectively. Moreover, the results showed that the frequency ratio model had the highest AUC in comparison with other models. Generally, the four models show a reasonable accuracy in flood-susceptible areas. The results of this study can be useful for managers, researchers, and planners to manage the susceptible areas to flood and reduce damages.

Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms

Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India

Flood susceptibility assessment using GIS-based support vector machine model with different kernel types

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region.

Performances of multi-criteria decision-making techniques in prediction of flood susceptibility are varied. We evaluated performances of ARAS, CODAS, COPRAS, EDAS, MOORA, TOPSIS, VIKOR, and WASPAS in predicting flood susceptibility of Barpeta district of Assam, India. Elevation, slope, proximity to river, geomorphology, drainage density, rainfall, land use/land cover, lithology, soil, stream power index, topographic wetness index and plan curvature were used as flood conditioning factors. The results show higher flood susceptibility over areas characterized by gentle slopes, low elevation and high proximity to drainage. Performances of the models were evaluated using 216 locations (flood and non-flood conditions) randomly classified into training (70%) and validation (30%) through area under receiver operating characteristic (ROC) curve (AUC). TOPSIS model showed better success (AUC = 0.965) and prediction rate (AUC = 0.962) than other models. Among the best performing models, highest percentage of area under high flood susceptibility was predicted by TOPSIS. Therefore, TOPSIS can be effectively used for flood risk management in areas having similar geographical conditions.