摘要
研究旨在基于随机森林-特征递归消除模型,通过SHAP算法(SHapley Additive exPlanation,SHAP)与部分依赖图(Partial Dependence Plot,PDP)对缓丘岭谷地貌区域进行滑坡易发性评价与内部机制解释,以期为地质灾害防治研究提供参考。利用优化随机森林算法对典型缓丘岭谷地区滑坡易发性进行研究,建立缓丘岭谷滑坡易发性评价模型;利用特征递归消除算法剔除噪声因子,选取地形地貌、地质构造、环境条件、人类活动5个类型16个因子构建重庆合川区滑坡致灾因子数据库;结合合川区754个历史滑坡点,利用随机森林算法对因子重要性进行排序,并根据专家经验法对研究区的滑坡易发性进行划分,将研究区的滑坡易发性分为极低、低、中、高、极高5个等级;应用部分依赖图对合川区滑坡发生影响大的因子进行解释和SHAP算法对个体滑坡进行局部解释。结果表明:与原模型相比,随机森林-特征递归消除模型测试集AUC值提高了0.019,证明了特征递归消除算法的有效性;训练集以及测试集的AUC值分别为0.769、0.755,具有较高的预测精度;缓丘缓坡地区在起伏较大地区滑坡密度较大,历史滑坡多集中于高易发地区;滑坡的空间分布具有不均匀性与复杂性,各致灾因子对滑坡发生的影响有着明显的区域特征与空间异质性,在缓坡丘陵地区多年平均降雨、高程、岩性3个因子对滑坡发生的影响最大;由SHAP算法对合川白塔坪上山公路滑坡事件进行解释,岩性与高程对滑坡起抑制作用,起伏度、坡度、归一化植被指数(NDVI)与POI核密度促进滑坡发生。综上所述,基于随机森林-特征递归消除模型在缓丘岭谷区滑坡易发性评价中具有较高的准确性,通过部分依赖图与SHAP算法对全局滑坡与个体滑坡发生的内在机理进行解释分析,有利于构建与完善不同地貌环境下滑坡易发性评价因子体系并探究滑坡内部决策机理,可为区域滑坡易发性评估与地质灾害防治提供参考。
This study aims to evaluate landslide susceptibility and explain the internal mechanism of gentle hillvalley through SHAP partial interpretation and PDP partial dependency map based on the random forest-recursive feature elimination model to provide references for geological disaster prevention and control.We used the optimized random forest algorithm to analyze the landslide susceptibility of the specific hill-valley areas and established a landslide susceptibility evaluation model.The recursive feature elimination algorithm was used to eliminate noise factors.Sixteen factors of four types,including terrain,geology,environmental conditions,and human activities,were selected to build a landslide hazard factor database for the Hechuan district.Then we combined 754 historical landslide sites in the Hechuan district with the factor database to derive a landslide susceptibility zoning map for the study area,and the factor importance was ranked using the random forest algorithm.Finally,a partial dependency plot is applied to explain the factors strongly influencing landslide occurrence in the Hechuan district and the SHAP algorithm for a local explanation of individual landslides.The results show that:compared with the original model,the AUC value of the test set of the random forest-recursive feature elimination model has increased by 0.019,demonstrating the effectiveness of the recursive feature elimination algorithm.According to the evaluation results of the random forest model,the AUC values of the training set and the test set are 0.769 and 0.755,respectively,with high prediction accuracy.The density is high in areas with large undulations,and historical landslides are concentrated in high-susceptibility areas.The spatial distribution of landslides is uneven and complex,and the influence of each hazard factor on landslide occurrence has prominent regional characteristics and spatial heterogeneity.In hill-valley areas,the average annual rainfall,elevation,and lithology are the most critical factors affecting landslide occurrence.According to the local interpretation map of SHAP,the landslide on the uphill road of Baitaping is explained.The lithology and elevation played a role in restraining the landslide,and the undulation,slope,NDVI,and POI kernel density promoted the landslide.In summary,the random forest-recursive feature elimination model has high accuracy in landslide susceptibility evaluation in the hill-valley areas.The interpretation and analysis of the internal mechanism of the regional landslides and individual landslides through PDP and SHAP interpretation algorithms are conducive to constructing and improving the evaluation factor system for landslide susceptibility under different geomorphic environments.The internal decision-making mechanism of landslides is explored;it can provide a reference for the regional landslide susceptibility assessment and geological disaster prevention.
作者
孙德亮
陈丹璐
密长林
陈星宇
密士文
李晓琴
SUN Deliang;CHEN Danlu;MI Changlin;CHEN Xingyu;MI Shiwen;LI Xiaoqin(School of Geography and Tourism,Chongqing Normal University,Chongqing 401331,China;Chongqing Key Laboratory of GIS Application,Chongqing 400040,China;Linyi Natural Resources Development Service Center,Linyi 276000,Shandong,China)
出处
《地质力学学报》
CSCD
北大核心
2023年第2期202-219,共18页
Journal of Geomechanics
基金
重庆市自然科学基金(cstc2020jcyj-msxmX0841)
教育部人文社科规划基金(20XJAZH002)
国家社会科学基金(22BJY140)。
关键词
滑坡易发性区划
随机森林算法
缓丘岭谷区
特征递归消除算法
部分依赖图
SHAP算法
landslide susceptibility mapping
random forest algorithm
gentle hill-valley area
recursive feature elimination algorithm
Partial Dependence Plot
SHapley Additive exPlanation algorithm
