Prediction Of Flood Susceptibility Research Articles

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

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

AbstractFlooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions.

Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan

Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan

Modeling and Mapping of Flood Susceptibility at Que Son District, Quang Nam Province, Vietnam using CatBoost

In this research, the main objective is to model and map flood susceptibility in Que Son district, Quang Nam province, Vietnam using one of the effective machine learning model namely CatBoost. With this purpose, a total of 96 flood and non-flood locations and a set of 10 conditioning factors were collected to construct the geospatial database. Thereafter, Shap feature importance method was used to validate and select the most important conditioning factors used for modeling of flood susceptibility, and the results showed that only 8 conditioning factors including aspect, slope, curvature, elevation, land cover, rainfall, distance to rivers, and Topographic Wetness Index (TWI) were selected for final modelling of flood susceptibility at the study area. Validation of the model was also done using various statistical indexes including area under the ROC curve (AUC). Validation results showed that the performance of CatBoost model (AUC = 0.96 for training and AUC = 0.94 for testing) is good for prediction of flood susceptibility of the study area. Thus, it can be concluded that CatBoost is valuable tool for flood susceptibility modeling which can be used to assess flood susceptibility in other flood prone areas of the world. In addition, flood susceptibility map generated from CatBoost model in this study might be helpful in development of better flood mitigation strategies at the study area.

Flood susceptibility prediction using tree-based machine learning models in the GBA

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) frequently suffered from floods accompanied with typhoons. This study developed a framework for evaluating flood susceptibility in the GBA using tree-based machine learning (ML) and geographical information system techniques. Based on the flood inventory, tree-based models, namely random forest, gradient boost decision tree, extreme gradient boosting, and categorical boosting considering topography, exposure, and vulnerability as influential factors, were used to train and test ML models, and the trained models were then used to predict flood susceptibility. All tree-based ML models achieved good performance, with accuracy values greater than 0.79. The categorical boosting model performed the best than other models to predict flood susceptibility. The flood susceptibility maps showed that more than 16% of the areas of the GBA were classified as having high flood susceptibility, and almost 70% of the historical floods were located in areas with high flood susceptibility. The model interpretation of the summary of Shapley additive explanation values indicated that the influential factors of elevation, population density, and typhoon intensity had a strong influence on flood susceptibility. The obtained spatial flood susceptibilities provide suggestions for flood disaster mitigation in the GBA.

Evaluation of flood susceptibility prediction based on a resampling method using machine learning

AbstractThe largest recorded flood loss occurred in the study area in 2013. This study aims to examine resampling methods (i.e. cross-validation (CV), bootstrap, and random subsampling) to improve the performance of seven basic machine learning algorithms: Generalized Linear Model, Support Vector Machine, Random Forest (RF), Boosted Regression Tree, Multivariate Adaptive Regression Splines, Mixture Discriminate Analysis, and Flexible Discriminant Analysis, and found the factors causing flooding and the strongest correlation between variables. The model is evaluated using Area Under the Curve, Correlation, True Skill Statistics, and Deviance. This methodology was applied in Kendari City, an urban area that faced destructive floods. The evaluation results show that CV-RF has a good performance in predicting flood susceptibility in this area with values, AUC = 0.99, COR = 0.97, TSS = 0.90, and deviance = 0.05. A total of 89.44 km2 or equivalent to 32.54% of the total area is a flood-prone area with a dominant area of lowland morphology. Among the 17 parameters that cause flooding, this area is strongly influenced by the vegetation density index and the Terrain Roughness Index (TRI) in the 28 models. The strongest correlation occurs between the TRI and the Sediment Transport Index (STI) = 0.77, which means that flooding in this area is strongly influenced by elements of violence.

Application of hybrid model-based deep learning and swarm‐based optimizers for flood susceptibility prediction in Binh Dinh province, Vietnam

Application of hybrid model-based deep learning and swarm‐based optimizers for flood susceptibility prediction in Binh Dinh province, Vietnam

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility.

Stormwater management modeling and machine learning for flash flood susceptibility prediction in Wadi Qows, Saudi Arabia

Stormwater management modeling and machine learning for flash flood susceptibility prediction in Wadi Qows, Saudi Arabia

Flood susceptibility prediction using multi criteria decision analysis and bivariate statistical models: a case study of Lower Kosi River Basin, Ganga River Basin, India

Flood susceptibility prediction using multi criteria decision analysis and bivariate statistical models: a case study of Lower Kosi River Basin, Ganga River Basin, India

자료기반 학습모델을 활용한 홍수예측에 관한 문헌연구

본 연구는 자연재난 중에서 홍수발생 예측을 위해 기계학습 및 딥러닝 등 자료기반 모델을 활용한 기존 문헌의 검토를 통해 홍수발생 영향요인 및 관련 공간정보 자료, 분석 방법과 모델 등의 특성을 분석하였다. 연구 결과에 따르면 영향요인은 크게 지형, 수문, 기상, 지질, 토양, 토지이용 등으로 유형화될 수 있었다. 특히 지형 관련 세부요인의 활용 비중이 상대적으로 높았으며, 이들은 원천 자료인 수치고도모델로부터 산출되는 사례가 많았다. 모델의 학습을 수행하기 전에 영향요인 자료 간 다중공선성의 조사 및 홍수발생에 관한 각 자료의 설명력을 정량적으로 산출하였다. 최근 들어 의사결정, 통계, 기계학습 및 딥러닝 기반 하이브리드 모델과 단일 모델의 최적화 기법을 개발하는 경향성이 뚜렷하였다. 이러한 선행연구의 특성을 고려할 때 향후 연구에서 고해상도 원천 자료의 수집 및 구축, 세부요인 자료의 분류 방법과 계급의 수에 대한 민감도 분석이 수행될 필요성이 제기된다. 그리고 홍수예측을 위한 다양한 하이브리드 모델 및 최적화 기법의 개발도 향후 연구에서 다루어져야 할 것이다.

Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran

Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran

GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le–Kien Giang watershed, Vietnam

GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le–Kien Giang watershed, Vietnam

Assessment of flood susceptibility prediction based on optimized tree-based machine learning models

Abstract Due to the physical processes of floods, the use of data-driven machine learning (ML) models is a cost-efficient approach to flood modeling. The innovation of the current study revolves around the development of tree-based ML models, including Rotation Forest (ROF), Alternating Decision Tree (ADTree), and Random Forest (RF) via binary particle swarm optimization (BPSO), to estimate flood susceptibility in the Maneh and Samalqan watershed, Iran. Therefore, to implement the models, 370 flood-prone locations in the case study were identified (2016–2019). In addition, 20 hydrogeological, topographical, geological, and environmental criteria affecting flood occurrence in the study area were extracted to predict flood susceptibility. The area under the curve (AUC) and a variety of other statistical indicators were used to evaluate the performances of the models. The results showed that the RF-BPSO (AUC=0.935) has the highest accuracy compared to ROF-BPSO (AUC=0.904), and ADTree-BPSO (AUC=0.923). In addition, the findings illustrated that the chance of flooding in the center of the area in question is greater than in other points due to lower elevation, lower slope, and proximity to rivers. Therefore, the ensemble framework proposed here can also be used to predict flood susceptibility maps in other regions with similar geo-environmental characteristics for flood management and prevention.

Integration of RS-GIS with Frequency Ratio, Fuzzy Logic, Logistic Regression and Decision Tree Models for Flood Susceptibility Prediction in Lower Gangetic Plain: A Study on Malda District of West Bengal, India

Integration of RS-GIS with Frequency Ratio, Fuzzy Logic, Logistic Regression and Decision Tree Models for Flood Susceptibility Prediction in Lower Gangetic Plain: A Study on Malda District of West Bengal, India

Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River basin, Alabama, United States

This study proposes a novel flash flood susceptibility prediction framework with a particular emphasis on the extent of imbalance between the number of flooding and non-flooding events as majority of the events result in non-flooding. The class imbalance issue and the magnitude of the imbalance was explored in this study to highlight the uncertain nature of the flooding phenomenon. Therefore, the Random Forest (RF) was initially adopted to evaluate five imbalance class distribution scenarios (i.e., 1x, 10x, 25x, 50x, 100x non-flood events, for each x flood event). Parameter configurations of developed models were determined with the state-of-the-art metaheuristic, the Cuckoo Search (CS) algorithm. The CS-RF model showed the highest (0.8455) prediction capability with regards to the area under the receiver operating characteristic (AUROC) once the extent of imbalance was set as 50x. The CS-RF model was then benchmarked with another bagging, i.e., Extra Trees, and two boosting, i.e., Adaptive Boosting (Adaboost) and eXtreme Gradient Boosting (XGBoost) algorithms, all integrated with the CS technique. Analysis results showed that the CS-RF is the most promising tree-based machine learning technique in flash flood susceptibility projection for the selected study area. According to the predictions, a flash flood susceptibility map was generated, where 9.35% of the basin was under very high flash flood risk. A recently developed model-agnostic game-theoretical method, SHapley Additive exPlanations (SHAP), was used for anatomizing the flash flood conditioning factors to highlight the contribution of each feature on the incident outcome prediction ensuring the transparency of the model findings. Overall, this study contributes to both theory and practice with particular focus on the model interpretability and existence of imbalance in the occurrence of flash flood events, assisting decision-makers in enhancing strategies to combat hazardous impacts of floods.

Topography based flood susceptibility prediction by using the GIS platform and historical flood data of lower Ajay river basin

Flood inundation mapping of lower Ajay River basin was implemented in the Geographic Information System (GIS) using the historical flood data and statistical frequency analysis by following the Gumbel probability distribution method. The historical data proofs the magnitudes of the flood intensity by various recurrence intervals on the basis of the projected flood height and present topographic character inundation mapping done in the GIS platform. Most importantly, due to the lack of hydrological past gauge height data are used in different gauge stations for projecting flood inundation mapping. Open access SRTM DEM data provides the topographic characteristics of the basin region and by the combined data of flood magnitudes and topographic data, flood inundation-mapping may help in the identification of the hazards prone area. The study area is regularly flooded and due to high population density huge damage takes place. So for the better management planning and risk estimation, hazards mapping is essential. Flood hazard mapping is done on the basis of 2, 10 and 25-year return period. The result depicts that 44% area in the lower part of the basin is flood prone and almost more than 474000 population and almost 59000 child population will be affected during the flood. The model is validated followed by overall accuracy assessment and KAPPA statistics methods and both results above 80% proof the model suitability.

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.

Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility

Identifying flood prone areas is essential for basin management. In this paper, a spatial prediction technology of flood susceptibility based on multiple kernel learning (MKL) is proposed. We establish the flood susceptibility model by using EasyMKL, nonlinear MKL (NLMKL), Representative MKL(RMKL), Generalized MKL(GMKL), support vector machine(SVM) with linear kernel and SVM with Gaussian radial base function(RBF) kernel, The spatial prediction of flood susceptibility in Sanhuajian basin of the Yellow River is carried out. We use MODIS remote sensing images to obtain historical flood inundation sites in the study area. Then, ten flood conditioning factors are used as inputs to the flood susceptibility model. The model performance is evaluated in terms of accuracy (ACC), balanced F Score (F1 score), and areas under the curve (AUC). According to the results, MKL significantly outperforms the SVM adopting single kernel, and NLMKL(ACC=0.833,F1=0.841,AUC=0.889) demonstrates the best comprehensive performance. The flood susceptibility map generated by MODIS remote sensing images and MKL, therefore, can provide effective help for researchers and decision makers in flood management.

Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.