Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Flooding is the most dangerous of all environmental hazards and leads to significant impacts both on the environment and on human life. Environmental protection and water management can be achieved by modeling flood susceptibility, to inform strategy that will reduce flood damage. The aim of this study is to develop novel hybrid models based on a radial basis functions neural network (RBFNN), arithmetic optimization algorithm (AOA), artificial bee colony (ABC), and ant lion optimizer (ALO), to build flood susceptibility maps in the Quang Tri province of Vietnam. The obtained models were trained and validated using 1511 flood points and 14 conditioning factors. Various statistical indices were used to assess the performance of the models, namely root mean square error, receiver operation characteristics (ROC), area under the receiver operating characteristic curve (AUC), and the coefficient of determination (R-2). The comparison analysis highlights that the RBFNN-ABC model was better than other models with an AUC of 0.98, followed by RBFNN-AOA, support vector machine, and random forest, all with an AUC value of 0.96, and finally RBFNN-ALO, with an AUC of 0.95. From the flood susceptibility map, it can be seen that the eastern areas of the study area have high and very high flood susceptibility, that requires local government attention. The approach and results of this study can support national and local authorities, decision-makers, and other planners in the construction of appropriate strategies to reduce potential damage in the future.