Susceptibility Modeling and Potential Risk Analysis of Thermokarst Hazard in Qinghai–Tibet Plateau Permafrost Landscapes Using a New Interpretable Ensemble Learning Method

• Published in: Atmosphere
• Main Authors: Yuting Yang, Jizhou Wang, Xi Mao, Wenjuan Lu, Rui Wang, Hao Zheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: thermokarst hazard; susceptibility evaluation; interpretability; potential risk analysis; ensemble learning; Qinghai–Tibet Plateau; Meteorology. Climatology; QC851-999; permafrost; Thermokarst
• Online Access: https://doi.org/10.3390/atmos15070788; https://doaj.org/article/1930403c28684cd9b1ed175651141212

Climate change is causing permafrost in the Qinghai–Tibet Plateau to degrade, triggering thermokarst hazards and impacting the environment. Despite their ecological importance, the distribution and risks of thermokarst lakes are not well understood due to complex influencing factors. In this study, we introduced a new interpretable ensemble learning method designed to improve the global and local interpretation of susceptibility assessments for thermokarst lakes. Our primary aim was to offer scientific support for precisely evaluating areas prone to thermokarst lake formation. In the thermokarst lake susceptibility assessment, we identified ten conditioning factors related to the formation and distribution of thermokarst lakes. In this highly accurate stacking model, the primary learning units were the random forest (RF), extremely randomized trees (EXTs), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost) algorithms. Meanwhile, gradient boosted decision trees (GBDTs) were employed as the secondary learning unit. Based on the stacking model, we assessed thermokarst lake susceptibility and validated accuracy through six evaluation indices. We examined the interpretability of the stacking model using three interpretation methods: accumulated local effects (ALE), local interpretable model-agnostic explanations (LIME), and Shapley additive explanations (SHAP). The results showed that the ensemble learning stacking model demonstrated superior performance and the highest prediction accuracy. Approximately 91.20% of the total thermokarst hazard points fell within the high and very high susceptible areas, encompassing 20.08% of the permafrost expanse in the QTP. The conclusive findings revealed that slope, elevation, the topographic wetness index (TWI), and precipitation were the primary factors influencing the assessment of thermokarst lake susceptibility. This comprehensive analysis extends to the broader impacts of thermokarst hazards, with the identified high and very high susceptibility zones ...