Flood susceptibility mapping using advanced hybrid machine learning and CyGNSS: a case study of Nghe An province, Vietnam

Flooding is currently the most dangerous natural hazard. It can have heavy human and material impacts and, in recent years, flooding has tended to occur more frequently, due to changes our species has made to hydrological regimes, and due to climate change. It is of the utmost importance that new models are developed to predict and map flood susceptibility with high accuracy, to support decision-makers and planners in designing more effective flood management strategies. The objective of this study is the development of a new method based on state-of-the-art machine learning and remote sensing, namely random forest (RF), dingo optimization algorithm, a weighted chimp optimization algorithm (WChOA), and particle swarm optimization to build flood susceptibility maps in the Nghe An province of Vietnam. The CyGNSS system was used to collect soil moisture data to integrate into the susceptibility model. A total of 1650 flood locations and 14 conditioning factors were used to construct the model. These data were divided at a ratio of 60/20/20 to train, validate, and test the model, respectively. In addition, various statistical indices, namely root-mean-square error, receiver operation characteristic, mean absolute error, and the coefficient of determination (R2), were used to assess the performance of the model. The results for all the models were good, with an AUC value of + 0.9. The RF-WChOA model performed best, with an AUC value of 0.99. The proposed models can predict and map flood susceptibility with high accuracy.