Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning

Abstract

The global warming and climate changes determined a considerable increase in the frequency of floods and their related damages. Therefore, the high accuracy prediction of flood susceptible areas plays a key role in flood warnings and risk reduction. The main objective of this study is to propose novel hybridizations of fuzzy Analytical Hierarchy Process (FAHP), Index of Entropy (IoE), and Support Vector Machine (SVM) for predicting the areas susceptible to floods. Buzău river catchment (Romania) was the area on which the present study was focused. In this regard, a database with 205 flooded locations, 205 non-flood locations and 12 flood predictors was established and used to train and validate the flood susceptibility models. The performance of the proposed models was evaluated using the Receiver Operating Characteristic (ROC) Curve and statistical metrics. The results show that all the hybrid models have a high prediction performance and outperform the stand-alone models. Among them, the SVM-IoE model (AUC = 0.979) has the highest performance, followed by the FAHP-IoE (AUC = 0.97), IoE (AUC = 0.969), SVM (AUC = 0.966) and FAHP (AUC = 0.947). These results highlight a very high efficiency of all the applied models. The application of the models mentioned above revealed that a percentage between 12.5% (FPIIoE) and 21.2% (FPIFAHP) of the study area is characterized by high and very high exposure to these hydrological hazards.