Affiliation:
1. a Faculty of Sciences Semlalia, Department of Geology, 2GRNT Laboratory (Geosciences, Geotourism, Natural Hazards and Remote Sensing), Cadi Ayyad University, Marrakech, Morocco
2. b Interuniversity Institute for Earth System Research (IISTA), University of Granada, Granada 18006, Spain
Abstract
ABSTRACT
Flash floods are highly destructive disasters, posing severe threats to lives and infrastructure. In this study, we conducted a comparative analysis of bivariate and multivariate statistical models and machine learning to predict flash flood susceptibility in the flood-prone Rheraya watershed. Six models were utilized, including frequency ratio (FR), logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), and naïve Bayes (NB). We considered 12 flash flood conditioning variables, such as slope, elevation, distance to the river, and others, as independent variables and 246 flash flood inventory points recorded over the past 40 years as dependent variables in the modeling process. The area under the curve (AUC) of the receiver operating characteristic was used to validate and compare the performance of the models. The results indicated that distance to the river was the most contributing factor to flash floods in the study area. Moreover, the RF outperformed all the other models, achieving an AUC of 0.86, followed by XGBoost (AUC = 0.85), LR (AUC = 0.83), NB (AUC = 0.76), KNN (AUC = 0.75), and FR (AUC = 0.72). The RF model effectively pinpoints highly susceptible zones, which is critical for establishing precise flash flood mitigation strategies within the region.
Reference112 articles.
1. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees
2. CTI Engineering International Co., Ltd., and Secrétariat d'Etat chargé de l'Eau et de l'Environnement;Agence Japonaise de Coopération Internationale (JICA),2011
3. Susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and bat algorithms (BA);Ahmadlou;Geocarto Inernational,2018
4. Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data
5. Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness