采煤沉陷区由于其特殊的地质条件和采煤背景，极易引发地面裂缝和地面塌陷，进而导致滑坡等地质灾害，选择适用的影响因子和训练模型在该地区开展滑坡敏感性评价十分必要。

以北京西山煤矿区为例，采用斜坡单元与数学统计模型和机器学习模型耦合方法，开展采煤沉陷区的滑坡敏感性评价。在地理信息系统环境中，选取坡度、坡向、地表湿润度等地形地貌因子，并加入地质背景的地层岩性和与断层距离因子，采煤背景的与采煤巷道距离和与井口距离等共10个因子作为评价指标，通过相关性分析优化滑坡敏感性评价体系。同时以水文分析法划分的地形斜坡单元为评价基础，分别应用信息量模型(I)信息量−随机森林(I-RF)、信息量−多层感知机(I-MLP)耦合模型对滑坡敏感性进行空间预测。

结果表明，耦合模型(I-RF、I-MLP)的精度均高于独立模型(I)，3种模型的AUC值分别为0.861、0.845、0.761，I-RF模型具有更强的预测能力和精度。此外地质和采煤背景因子的加入，优化了滑坡敏感性的评估效果。为了验证滑坡敏感性分区结果的实用性，以北京市“23·7”强降雨事件为时间节点，利用大比例尺航空摄影测量、时序InSAR技术等手段对滑坡敏感性评估结果进行佐证。结果表明，基于斜坡单元和耦合模型的滑坡敏感性评估结果与诱导事件后的滑坡灾害发育情况，有很大程度的吻合性。滑坡敏感性评价结果在一定程度上可以反映各斜坡的滑坡发生概率，对于采煤沉陷区滑坡灾害的预测和防治工作的开展有一定的参考性。

Due to their special geological conditions and coal mining background, coal mining subsidence areas are prone to suffer from ground cracks and subsidence, which tend to further induce geological disasters such as landslides. This makes it extremely necessary to conduct landslide sensitivity assessment of these areas using appropriate influencing factors and training models.

This study investigated the Xishan coal mine area in Beijing as an example to conduct landslide sensitivity assessment of coal mining subsidence areas by coupling slope units with mathematical statistical and machine learning models. With 10 influencing factors, including topographic and geomorphic factors such as slope, aspect, and surface wetness, geological background factors like formation lithology and fault throw, and coal mining background factors such as distances from coal mine roadways and wellheads, as assessment indices, this study optimized the landslide sensitivity assessment systems using correlation analyses. Meanwhile, based on the topographic slope units determined using hydrological analysis, this study predicted the spatial landslide sensitivity using the information value (I) model, the information value - random Forest (I-RF) coupling model, and the information value - multi-layer perceptron (I-MLP) coupling model individually. [Results and conclusions] The results indicate that the I-RF and I-MLP coupling models exhibited higher accuracy than the independent I model. The I-RF, I-MLP, and I models exhibited areas under the curve (AUCs) of 0.861, 0.845, and 0.761, respectively, suggesting that the I-RF model enjoys the highest predictive ability and accuracy. Additionally, the effects of landslide sensitivity assessment were improved due to the introduction of geological and coal mining background factors. To further verify the practicality of the landslide sensitivity zoning, this study, using the “23.7” extreme rainfall event in Beijing- as a case study, compared the landslide sensitivity results obtained using the models and the landslide hazards determined using techniques such as large-scale aerial photogrammetry and time-series InSAR. The verification results indicate that the landslide sensitivity assessment results based on slope units and coupling models agree well with the landslide hazards triggered by the rainfall event. Therefore, the landslide sensitivity assessment results can reflect the landslide probability of various slopes to a certain extent, thus serving as a reference for the prediction and prevention of landslide hazards in coal mining subsidence areas.