Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: in a case study of Tana sub-basin in northwestern Ethiopia

Abstract. The sub-basin of Lake Tana is one of the most flood-prone areas in northwestern Ethiopia, which is affected by flood hazards. Flood susceptibility modeling in this area is essential for hazard reduction purposes. For this, the analytical hierarchy process (AHP), bivariate, and multivariate statistical methods were used. Using an intensive field survey, historical record, and Google Earth Imagery, 1404 flood locations were determined which are classified into 70 % training datasets and 30 % testing flood datasets using subset in the GIS tool. The statistical relationship between the probability of flood occurrence and eleven flood-driving factors is performed using the GIS tool. Then, the flood susceptibility map of the area is developed by summing all weighted factors using a raster calculator and classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The results for the area under the curve (AUC) are 99.1 % for the frequency ratio model is better than 86.9 % using AHP, 81.4 % using the logistic regression model, and 78.2 % using the information value model. Based on the AUC values, the frequency ratio (FR) model is relatively better followed by the AHP model for regional flood use planning, flood hazard mitigation, and prevention purposes.