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A Hybrid Feature Selection Approach for Flash Flood Disaster Risk in Jiangxi Province, China

Appropriate feature selection is critical for flash flood disaster risk assessment as the premise of flash flood warning and forecast. Furthermore, a thorough examination of the algorithm for selecting flash flood risk features and the handing of unbalanced data is required. This study proposes a practical hybrid feature selection approach based on the combination of random forests (RF) and simulated annealing (SA) with a case study in Jiangxi Province, China. By employing weighted samples as the input to the RF-model, this method reduces the influence of unbalanced samples. Results indicate that: The optimal weighting ratios for terrain features are 3:1 or 4:1. The optimal weighting ratios for rainfall features are 2:1 or 3:1. The findings of the two algorithms are combined with physical interpretation of features and concentration time in mountainous catchments, resulting in the selection of four terrain features: slope, maximum elevation, outlet catchment area, confluence time, and four rainfall features: the annual maximum variation coefficients of 10 minutes, 6 hours (CV_M10, CV_H6), and the mean of annual maximum rainstorm of 10 minutes, 6 hours (EX_M10, EX_H6).