Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam

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 measures were used to validate the models, including the Area Under Receiver Operating Characteristic (ROC) Curve called AUC. Results show that all the proposed models performed well, but the performance of the BDT model (AUC = 0.96) is the best in comparison to other models ABMDT (AUC = 0.953) and single DT (AUC = 0.929). Therefore, the flood susceptibility map produced by the BDT model was used to combine with a flood consequences map to develop a reliable flood risk assessment map for the study area. The final flood risk map can provide a useful source for better flood hazard management of the study area, and the proposed framework and models can be applied to other flood-prone areas.

Automated summary

This study proposed a flood risk assessment framework combining flood susceptibility assessment and flood consequences using Multi-Criteria Decision Analysis (MCDA) method. Two hybrid AI models, ABMDT and BDT, were developed with Decision Table (DT) as a base classifier to create a flood susceptibility map. The BDT model performed the best with an AUC of 0.96, and the proposed framework and models can be applied to other flood-prone areas for better flood hazard management.