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