Flood risk assessment using machine learning, hydrodynamic modelling, and the analytic hierarchy process

Abstract

ABSTRACT The objective of this study was to develop a theoretical framework based on machine learning, the hydrodynamic model, and the analytic hierarchy process (AHP) to assess the risk of flooding downstream of the Ba River in the Phu Yen. The framework was made up of three main factors: flood risk, flood exposure, and flood vulnerability. Hazard was calculated from flood depth, flood velocity, and flood susceptibility, of which depth and velocity were calculated using the hydrodynamic model, and flood susceptibility was built using machine learning, namely, support vector machines, decision trees, AdaBoost, and CatBoost. Flood exposure was constructed by combining population density, distance to the river, and land use/land cover. Flood vulnerability was constructed by combining poverty level and road density. The indices of each factor were integrated using the AHP. The results showed that the hydraulic model was successful in simulating flood events in 1993 and 2020, with Nash–Sutcliffe efficiency values of 0.95 and 0.79, respectively. All machine learning models performed well, with area under curve (AUC) values of more than 0.90; among them, AdaBoost was most accurate, with an AUC value of 0.99.