Application of artificial neural network, frequency ratio and evidential belief function models in preparing of flood susceptibility map in Haraz watershed: A plan for urban flood risk studies

In this study, artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) methods were used to prepare the flood susceptibility map. For this purpose, the parameters of ten, slope, land curvature, topographic moisture index, distance from river and geology and type of lands in Haraz watershed in Mazandaran province were performed. Eleven conditioning factors including slope, land curvature, distance to river, river density, elevation, rainfall, stream power index (SPI), topographic wetness index (TWI), lithology, land use and normalized difference vegetation index (NDVI) were used in Haraz watershed in Mazandaran province. In addition, 201 floodplains were located in the area. The points were randomly divided into groups of 141 points (70%) and 60 points (30%) for training and validation, respectively. Furthermore, the probability of flooding for each class of each factor was calculated. Hence, the weights obtained for each class in the Geographic Information System (GIS) were applied in the respective layers, and the flood susceptibility maps of the study area were obtained. Based on the flood susceptibility map, the area was divided into 5 classes with very high, high, medium, low and very low sensitivity. These methods were evaluated by area under the curve (AUC) method. The results indicate that the lower and near elevation to river have a high probability and sensitivity to flooding. The results of the current study showed that the frequency ratio (AUC = 0.97) and evidential belief function (AUC = 0.94) and artificial neural network (AUC = 0.87) methods had the highest accuracy in predicting flood occurrence, respectively. The results suggest that these models can be useful and reliable in predicting flood risk potential, especially in different areas, including urban spaces, due to their high efficiency.