Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

• Published in: Stochastic environmental research and risk assessment 2023-06, Vol.37 (6), p.2243-2270
• Main Authors: IBAN, Muzaffer Can, BILGILIOGLU, Suleyman Sefa
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Artificial intelligence; Avalanches; Chemistry and Earth Sciences; Classifiers; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Explainable artificial intelligence; Landslides; Learning algorithms; Machine learning; Math. Appl. in Environmental Science; Original Paper; Physics; Probability Theory and Stochastic Processes; Risk reduction; Ski resorts; Skiing; Slopes; Snow; Snow avalanches; Statistics for Engineering; Susceptibility; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-023-02392-6

This study examines the use of snow avalanche susceptibility maps (SASMs) to identify areas prone to avalanches and develop measures to mitigate the risk in the Province of Sondrio, Italy. Various machine learning classifiers such as Random Forest, Gradient Boosting Machines, and AdaBoost, as well as newer classifiers like XGBoost, LightGBM, and NGBoost, were used with 17 conditioning factors and 1880 snow avalanche samples. The XGBoost classifier was found to have the best performance and McNemar’s test results indicated that certain classifier pairs, such as RF-AdaBoost, RF-XGBoost, and XGBoost-LightGBM, produced significant predictions while others did not. The XGBoost classifier found that 19.31% of Sondrio was very susceptible to avalanches. Instead of providing a global explanation of the classifier models, the study employs a local eXplainable Artificial Intelligence (XAI) approach called SHapley Additive eXplanations (SHAP) to give insight into how each conditioning factor contributes to the likelihood of snow avalanches. According to the SHAP values, the three most important factors in the XGBoost classifier model for determining the likelihood of snow avalanches are elevation, maximum temperature, and slope. The model shows that as elevation increases, the likelihood of avalanches also increases. On the other hand, a higher maximum temperature is found to decrease the likelihood of an avalanche. Slope is found to have a positive effect on the likelihood of an avalanche, meaning that steeper slopes increase the likelihood of an avalanche. This study also analyzes the avalanche susceptibility of ski resorts in the province and found that the majority of them are located in low and moderately susceptible areas, but some are in highly susceptible areas. The study used SHAP force plots to examine the local factors that contribute to the likelihood of avalanches in these specific ski resorts. The results show that ski resorts with elevations greater than 2000 m and slopes greater than 30 degrees, such as Livigno, Santa Caterina-Valfurva and Passo dello Stelvio, have a higher susceptibility to avalanches due to higher positive SHAP values. Conversely, ski resorts with elevations less than 2000 m and slopes less than 30 degrees, such as Aprica and Bormio, have a lower susceptibility to avalanches because of negative SHAP values. This study provides a valuable tool for creating new strategies to reduce the harm and damage caused by slow avalanches in the