GIS ​– ​based flood susceptibility mapping using frequency ratio and information value models in upper Abay river basin, ​Ethiopia

Abstract

In this study, flood susceptibility mapping was carried out for Chemoga watershed upper Abay River basin, Ethiopia. The main objective of this study is to identify the flood susceptibility areas using Frequency ratio and Information Values models. Based on Google Earth imagery and filed survey, about 168 flooding locations were identified and classified randomly into training flood locations datasets 70% (118) and the remaining 30% (50) of flooding locations datasets were used for validation purpose. Identified 12, flood conditioning factors such as slope, elevation, aspect, curvature, TWI, NDVI, distance from road, distance from river, soil texture, lithology, land use and rainfall were integrated with training flood locations datasets to determine the weights of each flood location conditioning factor and factor classes using both frequency ratio and information value models. The flood susceptibility maps were produced by overlay the weights of all the flood conditioning factors using raster calculator of the spatial analyst tool in ArcGIS 10.4. The final flood susceptibility maps were reclassified as very low, low, moderate, high and very high susceptibility classes both FR and IV models. This susceptibility maps were validated using flood location area under the curve (AUC). The results of AUC accuracy models showed that the success rates of the FR and IV models were 82.90% and 82.10%, while the prediction rates were 80.70% and 80.00% respectively. Past flood events are compared with the flood vulnerable database to validate the modeled output in the present study. This type of study will be very useful to the local government for future planning and decision on flood mitigation plans.