Mapping flood susceptibility in mountainous areas on a national scale in China.

Mountainous terrain covers nearly half of China and is susceptible to floods, which can lead to substantial losses of human life and property. Historical flooding records from government bulletins and newspapers, the only available information regarding floods that have occurred in some mountainous areas, are valuable for understanding flood disaster mechanisms in these regions. In this study, the flood susceptibility in mountainous regions in China was mapped based on historical flooding records from 1949 to 2000. A Random Forest (RF) model, which can handle large datasets through factor contribution analysis, was chosen to characterize the relationships between flooding occurrences and twelve geographic, meteorological, and hydrological explanatory factors. The results indicate that the RF model can effectively identify flood-prone areas and has advantages over artificial neural network (ANN) and support vector machine (SVM) methods. Among these explanatory factors, the geographic factors (elevation, longitude and drainage density) are the most important predictors of flooding in China's mountainous areas, whereas the hydrological factors (relative elevation and curve number) are the least important. Two independent datasets of historical flooding events from the Bulletin of Flood and Drought Disasters in China (2006-2014) alongside news reports and yearbooks (2008-2014) were collected and chosen to validate the capability of the RF model. The validation results confirm that the RF model can identify the flood susceptibility with satisfactory accuracy. This study proposes a preliminary flood susceptibility map of mountainous areas in China and provides a reference for predicting and mitigating potentially disastrous flooding events.