滑坡影响因素的空间异质性是易发性评价模型泛化性差的主要原因。本研究旨在基于SHAP(SHapley加法解释)-XGBoost(eXtremeGradientBoosting)算法构建滑坡敏感性评估模型的综合解释框架,分析滑坡影响因素的区域特征和空间异质性,并讨论了不同景观下模型泛化的异质性。首先,选取典型山地丘陵区的不同区域,构建了包含海拔、年平均降雨量,斜坡,岩性,和NDVI通过实地调查,卫星图像,和文献综述。随后,基于XGBoost算法和空间数据库,构建了滑坡易发性评价模型,并根据区域地形解释了滑坡敏感性评价模型的预测结果,地质学,和水文学使用SHAP算法。最后,该模型被推广并应用于地形相似和非常不同的地区,地质学,气象学,和植被,探索模型泛化的空间异质性。得出以下结论:滑坡的空间分布具有异质性和复杂性,各影响因子对滑坡发生的贡献具有明显的区域特征和空间异质性。滑坡敏感性评价模型的泛化性具有空间异质性,对具有相似区域特征的区域具有较好的泛化性。使用SHAP方法对XGBoost滑坡易发性评价模型进行进一步解释,可以定量分析由于空间异质性,各种因素对灾害的贡献有多大的差异,从全球和地方评估单位的角度来看。总之,基于SHAP-XGBoost模型的综合解释框架可以量化影响因素对全球和地方滑坡发生的贡献,有利于不同区域滑坡易发性影响因素体系的构建和完善。它还可以为预测潜在滑坡灾害易发区和可解释人工智能(XAI)研究提供参考。
The spatial heterogeneity of landslide influencing factors is the main reason for the poor
generalizability of the susceptibility evaluation model. This study aimed to construct a comprehensive explanatory framework for landslide susceptibility evaluation models based on the SHAP (SHapley Additive explanation)-XGBoost (eXtreme Gradient Boosting) algorithm, analyze the regional characteristics and spatial heterogeneity of landslide influencing factors, and discuss the heterogeneity of the
generalizability of the models under different landscapes. Firstly, we selected different regions in typical mountainous hilly region and constructed a geospatial database containing 12 landslide influencing factors such as elevation, annual average rainfall, slope, lithology, and NDVI through field surveys, satellite images, and a literature review. Subsequently, the landslide susceptibility evaluation model was constructed based on the XGBoost algorithm and spatial database, and the prediction results of the landslide susceptibility evaluation model were explained based on regional topography, geology, and hydrology using the SHAP algorithm. Finally, the model was generalized and applied to regions with both similar and very different topography, geology, meteorology, and vegetation, to explore the spatial heterogeneity of the
generalizability of the model. The following conclusions were drawn: the spatial distribution of landslides is heterogeneous and complex, and the contribution of each influencing factor on the occurrence of landslides has obvious regional characteristics and spatial heterogeneity. The
generalizability of the landslide susceptibility evaluation model is spatially heterogeneous and has better
generalizability to regions with similar regional characteristics. Further explanation of the XGBoost landslide susceptibility evaluation model using the SHAP method allows quantitative analysis of the differences in how much various factors contribute to disasters due to spatial heterogeneity, from the perspective of global and local evaluation units. In summary, the integrated explanatory framework based on the SHAP-XGBoost model can quantify the contribution of influencing factors on landslide occurrence at both global and local levels, which is conducive to the construction and improvement of the influencing factor system of landslide susceptibility in different regions. It can also provide a reference for predicting potential landslide hazard-prone areas and for Explainable Artificial Intelligence (XAI) research.