Flood risk assessment using deep learning integrated with multi-criteria decision analysis

In this paper, we proposed a novel approach for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The framework of the flood risk assessment involves three main elements: hazard, exposure, and vulnerability. For this purpose, one of the flood-prone areas of Vietnam, namely Quang Nam province was selected as the study area. Data of 847 past flood locations of this area was analyzed to generate training and testing datasets for the models. In this study, we have used one of the popular Deep Neural Networks (DNNs) algorithm for generation of flood susceptibility map while Analytic Hierarchy Process (AHP), which is a popular MCDA approach, was used to generate the hazard, exposure, and vulnerability maps. We have also used hybrid models namely BFPA and DFPA which are the ensembles of Bagging and Decorate with Forest by Penalizing Attributes algorithm for the comparison of performance with DNNs method. Various standard statistical indices including Receiver Operating Characteristic (ROC) curves were used for the performance evaluation and validation of the models. Results indicated that integration of DNNs and MCDA models is a promising approach for developing accurate flood risk assessment map of an area for the better flood hazard management. •Deep learning integrated with multi-criteria decision analysis for flood risk assessment.•Flood risk framework includes integration of hazard, exposure, and vulnerability maps.•Deep learning algorithm outperformed other hybrid ML models for better flood risk assessment.