Elevating Flash Flood Prediction Accuracy: A Synergistic Approach with PSO and GA Optimization

Abstract. Flash floods are frequent and devastating natural disasters in small mountainous river basins worldwide, causing significant harm to people, infrastructure, and property. Flash flood susceptibility mapping is a crucial tool for damage prevention and reduction. This study is focused on the creation of flash flood susceptibility maps in a mountainous region in northern Vietnam. We enhanced the performance of robust machine learning models, including Support Vector Machines (SVM), Random Forests (RF), and XGBoost (XGB), by applying advanced optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). These models were developed based on 14 key factors, including elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), flow accumulation, river density, distance to the river, NDVI, land use/land cover (LULC), rainfall, soil type, geology, and 412 flood inventory points. Nine models were tested, including three standalone ML algorithms (SVM, RF, XGB), three ensemble models optimized with PSO (PSO-SVM, PSO-RF, PSO-XGB), and three optimized with GA (GA-SVM, GA-RF, GA-XGB). The results indicated that ensemble models outperformed standalone ones, with the PSO-XGB, GA-XGB, and GA-RF models exhibiting outstanding performance, achieving accuracy rates of 0.939, 0.927, and 0.933, along with remarkable AUC-ROC scores of 0.957, 0.968, and 0.977, respectively. This innovative study introduces a novel set of associative models, contributing significantly to the advancement of flood prediction techniques. The methodology holds applicability for various regions characterized by similar topographical and climatic attributes. Furthermore, enhancing the precision of flood forecasting contributes to the formulation of mitigation strategies by municipal authorities to mitigate prospective flood-related impacts.