Integrating Sequential Backward Selection (SBS) and CatBoost for Snow Avalanche Susceptibility Mapping at Catchment Scale

Abstract

Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. Using a sub-optimal set of input features with a data-driven machine learning (ML) method can lead to challenges like dealing with high-dimensional data, overfitting, and reduced model generalization. This study implemented a robust framework involving the Sequential Backward Selection (SBS) algorithm and a decision-tree based ML model, CatBoost, for the automatic selection of predictive variables for AS mapping. A comprehensive inventory of a large avalanche period, previously derived from satellite images, was used for the investigations in three distinct catchment areas in the Swiss Alps. The integrated SBS-CatBoost approach achieved very high classification accuracies between 94% and 97% for the three catchments. In addition, the Shapley additive explanations (SHAP) method was employed to analyze the contributions of each feature to avalanche occurrences. The proposed methodology revealed the benefits of integrating advanced feature selection algorithms with ML techniques for AS assessment. We aimed to contribute to avalanche hazard knowledge by assessing the impact of each feature in model learning.