基于XGBoost-SHAP模型的山西省煤炭国家规划矿区生态质量变化评估及驱动机制研究OACHSSCD

The nationally planned coal mining area in Shanxi Province is an important energy-rich and ecologically fragile area in the Yellow River Basin,and it has long been under the dual pressure of high-intensity coal mining and ecological environment protection.However,the spatiotemporal evolutionary characteristics and driving factors of its ecological quali-ty remain unclear,which seriously restricts precise implementation of regional ecological restoration policies.Based on multi-source remote-sensing data from 2000 to 2020,in this study,a remote-sensing ecological index of the mining area(M-RSEI)was established,and the XGBoost-SHAP interpretable framework was used to reveal the spatiotemporal evolu-tionary characteristics and nonlinear driving mechanisms of its ecological quality.The results show that:(1)M-RSEI is more robust and applicable to the coal-mining area environment than the traditional RSEI model,and it can accurately characterize the dynamic evolutionary characteristics of the entire life cycle of"mining-expansion-restoration"in the min-ing area.(2)From 2000 to 2020,the ecological quality of the coal mining areas was generally at a medium level and showed a steady improvement trend.The average value of M-RSEI increased from 0.453 to 0.560,presenting a spatial pattern of"high in the southeast and low in the northwest"in terms of space.(3)The XGBoost-SHAP attribution analysis indicated that annual potential evapotranspiration,annual precipitation,and land-use type are the key factors affecting the spatial differentiation of ecological quality in the study area,and the influence of each factor on ecological quality has sig-nificant nonlinear response characteristics and threshold effects.Our conclusions can provide a scientific reference for re-source-based regions to formulate different sustainable-development strategies and promote high-quality coordinated devel-opment of green mine construction and the ecological environment.