Bangladesh experiences frequent hydro-climatic disasters such as flooding. These disasters are believed to be associated with land use changes and climate variability. However, identifying the factors that lead to flooding is challenging. This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation (BRBP) neural network, classification and regression trees (CART), a statistical model (STM) using the evidence belief function (EBF), and their ensemble models (EMs) for three time periods (2000, 2014, and 2017). The accuracy of machine learning algorithms (MLAs), STM, and EMs were assessed by considering the area under the curve—receiver operating characteristic (AUC-ROC). Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone and other ensemble models for the three time periods analyzed. Furthermore, this study investigated the relationships among land cover change (LCC), population growth (PG), road density (RD), and relative change of flooding (RCF) areas for the period between 2000 and 2017. The results showed that areas with very high susceptibility to flooding increased by 19.72% between 2000 and 2017, while the PG rate increased by 51.68% over the same period. The Pearson correlation coefficient for RCF and RD was calculated to be 0.496. These findings highlight the significant association between floods and causative factors. The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.

Flooding and its relationship with land cover change, population growth, and road density

Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

Key coastal landscape patterns for reducing flood vulnerability.

Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm

Greater Mumbai is one of the major sufferers of urban flood in India. In the present study, flood susceptibility zonation (FSZ) mapping of Greater Mumbai were performed using Geographical Informati...

Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India

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Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India