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

This study aims at optimizing the support vector regression (SVR) model using four metaheuristic methods, Harris hawks optimization (HHO), particle swarm optimization (PSO), gray wolf optimizer (GWO), and bat algorithm (BA). The intent is to create a reliable flood susceptibility map (FSM). In this regard, a flood inventory map for 617 flood locations was generated from the Google earth engine (GEE). Four hundred and thirty-two random locations (70%) were used for spatial flood susceptibility modeling, and 185 random locations (30%) were selected for testing hybrid approaches. Based on the available data and literature, the following eleven factors were selected: altitude, slope angle, slope aspect, plan curvature, stream power index (SPI), topographic wetness index (TWI), distance to river, lithology, drainage density, land use, and rainfall. The normalized frequency ratio (NFR) method was used to obtain a weight for each class of each factor. Next, flood susceptibility maps were produced by SVR-HHO, SVR-PSO, SVR-GWO, and SVR-BA hybrid models. The prediction power of hybrid models was assessed using various indicators of sensitivity, specificity, accuracy, kappa coefficient, receiver operating curve (ROC) diagram, mean square error (MSE), and root-mean-square error (RMSE). Validation results indicated the area under the curve (AUC) of 85.8%, 85.7%, 85.5%, and 84.6% for the SVR-HHO, SVR-GWO, SVR-BA, and SVR-PSO hybrid models, respectively. The results from testing phase reveal the best performance of the SVR-HHO model (RMSE = 0.401, MSE = 0.160, sensitivity = 0.822, specificity = 0.800, accuracy = 0.811, and kappa = 0.622). The SVR-PSO model had a poor performance (RMSE = 0.406, MSE = 0.164, sensitivity = 0.827, specificity = 0.773, accuracy = 0.80, and kappa = 0.60). It can be concluded that the map produced by SVR-HHO is a feasible approach for modeling flood susceptibility.