Snow avalanche susceptibility assessment based on ensemble machine learning model in the central Shaluli Mountain

Abstract

The central part of the Shaluli Mountains is located in the Ganzi area, Sichuan Province, China, bordered by the Jinsha River and adjacent to Tibet. Frequent avalanches pose a serious threat to human activities and engineering construction such as the Sichuan-Tibet Railway under construction. Therefore, the evaluation of avalanche susceptibility in this area can not only help define the spatial pattern of avalanches on the Qinghai-Tibet Plateau but also provide references for the recognition and early warning of regional avalanche disasters. In this study, avalanche samples were selected by remote sensing interpretation supplemented by a detailed field survey, GIS spatial analysis, and data mining. Two statistical models [evidence confidence function (EBF) and certainty coefficient (CF)] combined with two machine learning models [logistic regression (LR) and multilayer perceptron (MLP)] were used to establish four integrated models (EBF-LR, CF-LR, EBF-MLP, and CF-MLP) as well as the traditional frequency ratio model (FR) for avalanche susceptibility evaluation. Finally, the results were checked for accuracy by Kappa coefficients and ROC curves. The CF-MLP (Kappa = 0.606, AUC = 0.910) model was the best avalanche susceptibility evaluation model for this study, the FR (Kappa = 0.584, AUC = 0.894) model had the next highest accuracy, and the combination of the CF algorithm and the machine learning model performed better than the EBF. The most important influencing factors were elevation, slope orientation, terrain moisture index, and January average temperature. The five models showed a high degree of consistency in the sensitivity to topographic factors during the evaluation of susceptibility. The avalanche susceptibility zoning map based on the CF-MLP model was obtained by the natural breakpoint method, and the areas with very high and high susceptibility accounted for about 10.01% and 15.33% of the total area, respectively.