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Flood risk assessment is an important task for disaster management activities in flood-prone areas. Therefore, it is crucial to develop accurate flood risk assessment maps. In this study, we proposed a flood risk assessment framework which combines flood susceptibility assessment and flood consequences (human health and financial impact) for developing a final flood risk assessment map using Multi-Criteria Decision Analysis (MCDA) method. Two hybrid Artificial Intelligence (AI) models, namely ABMDT (AdaBoost-DT) and BDT (Bagging-DT) were developed with Decision Table (DT) as a base classifier for creating a flood susceptibility map. We used 847 flood locations of major flooding events in the years 2007, 2009 and 2013 in Quang Nam province of Vietnam; and 14 flood influencing factors of topography, geology, hydrology and environment to construct and validate the hybrid AI models. Various statistical …