zoning of flood hazard in Nowshahr city using machine learning models

  The aim of this study is to predict and model flood hazard in the city of Nowshahr, Mazandaran province using machine learning models. The criteria and indicators affecting flood hazard were identified based on the review of resources, and then the indicators were converted into rasters in ArcGIS environment, and finally standardized by fuzzy method for use in the models. K-nearest neighbor models (Fine KNN, Cubic KNN, Cosine KNN, Weighted KNN), decision tree (Fine tree, Medium tree, Coarse tree), ensemble learning (Subspace KNN)) and WLC were used in the present study to predict flood hazard. ROC method was used to validate the models and select the best model. The layers were developed in the ArcGIS environment and transferred to MATLAB software in order to perform the modeling process. In this software environment, the values ​​of each model including determination coefficients and models were executed. The results showed that the ensemble learning model with an accuracy of 0.96 and area under curve of 0.90 is the best and the WLC model with an accuracy of 0.61 and area under curve of 0.62 is the weakest model of flood hazard prediction in Nowshahr.