Scrutinizing the performance of GIS-based analytical Hierarchical process approach and frequency ratio model in flood prediction–Case study of Kakegawa, Japan
Floods are one of the most common catastrophes in the world. This study generates the flood susceptibility maps (FSM) using AHP and FR in Kakegawa, Japan. A set of 100 flood points were presented in an ArcGIS environment where 70 points were chosen at random as a training dataset while 30 ones were used for validation. Eleven flood causative factors were calculated and utilized to generate the flood vulnerability maps. For the validation 30% data sub-sample set, FSM was completed by creating the receiver operating characteristic curve and the area under the curve (AUC). The results indicate that the two methods show sensible accuracy since the AUC for FR and AHP are 67% and 85.5% respectively. AHP showed higher accuracy due to the expert opinion that being shared while FR achieved lower precision because of its simple arithmetic procedures. The results help decision-makers in determining the locations vulnerable to flooding.
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