A novel framework for multiple thermokarst hazards risk assessment and controlling environmental factors analysis on the Qinghai-Tibet Plateau

• A novel framework for multi-thermokarst hazard risk assessment is proposed. • Significantly improved assessment accuracy compared with previous studies. • Stacking machine learning combined the strengths of multiple machine learning models. • Controlling environmental factors for thermokarst hazards were identified. Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. Currently, high-precision and harmonized assessment tools for multiple thermokarst hazards risk assessment are lacking, and the mechanisms governing the environmental interactions of thermokarst hazards have not been fully clarified. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). In addition, model performance was improved by model optimization. Finally, structural equation modeling (SEM) was used to assess the controlling environmental factors for the thermokarst hazards in the YRSR. The results show that slope and precipitation contribute the most to the modeling accuracy of thermokarst lakes and thaw slumps, respectively. Model optimization improved the base model modeling accuracy by approximately 2 % ∼ 7 %, with XGBoost having the highest sensitivity to model optimization and the highest modeling accuracy. In terms of the ensemble strategy, the stacking model and ensemble model significantly improved the risk mapping accuracy, and the stacking model was better than the ensemble model, with accuracies of 92.39 % and 93.36 % for thermokarst lakes and thaw slumps, respectively. Compared with previous results, the results of this study are more representative of the YRSR. Finally, via SEM, terrain factors and soil factors were identified as controlling environmental factors for the risk of thaw slumps and thermokarst lakes, respectively. This study proposes a high-precision risk assessment method for thermokarst hazards in permafrost regions, and contributes to a deeper understanding of the interaction mechanisms between thermokarst hazards and environmental factors.