Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees

Abstract

Accurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (Cc) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and Cc-Euclidean, 0.996 for Cc-Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, Cc-Euclidean, and Cc-Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.