Floods are one of the most perilous natural calamities that cause property destruction and endanger human life. The spatial patterns of flood susceptibility were assessed in this study using six applied machine learning (ML) models including Decision Tree (DT), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), Adaptive Boosting (AdaBoost), Logistic Regression (LR), and Support Vector Machines (SVM).The flood susceptibility map, as the principal output of this study, was produced using these six ML models in five classes ranging from very low to very high susceptibility.
Due to the large size of the input layers (flood conditioning factors), all were uploaded in Google Drive; they can be downloaded from the following link: https://drive.google.com/file/d/1t4lz41e5ttFzV2HLxsc9iAAZ44OPAo2J/view?usp=sharing