A study on landslide susceptibility of LightGBM-SHAP based on different factor screening methods
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摘要: 以重庆市黔江区为例，选取23个评价因子构建滑坡致灾因子数据库，利用地理探测器与皮尔逊-主成分分析2种因子筛选方法选择因子最优组合；基于Bayesian-LightGBM-SHAP混合模型进行滑坡易发性评价，并对模型精度进行验证，分析影响黔江区滑坡发生的主导因子．初始模型的AUC值为0.801，Person Correlation Coefficient-Bayesian-LightGBM模型AUC值为0.824，GeoDetector-Bayesian-LightGBM模型AUC为0.835；由因子重要性可知，多年平均降雨量、高程、POI核密度与距河流距离是滑坡发生的最主要因子，而输沙指数、水流动力指数与坡位对滑坡的发生影响较弱．因子筛选法-Bayesian-LightGBM相结合的混合模型能够提高模型的准确性，为构建合理因子数据库提供参考框架；通过与因子重要性的结合分析，验证了地理探测器能够准确探测各因子对滑坡发生的贡献值，突出各滑坡调理因子组合之间的相关性，从而探究各因子与滑坡之间的关系．Abstract: Taking Qianjiang District of Chongqing Municipality as an example, 23 evaluation factors are selected to construct a landslide disaster-causing factor database, and the optimal combination of factors is chosen by utilizing two kinds of factor screening methods, i.e., geodetector and Pearson-principal component analysis. Based on the Bayesian-LightGBM-SHAP hybrid model for landslide susceptibility evaluation, and verify the model accuracy, the dominant factors affecting the occurrence of landslides in Qianjiang District are analyzed. The initial model has an AUC value of 0.801, the Person Correlation Coefficient-Bayesian-LightGBM model has an AUC value of 0.824, and the GeoDetector-Bayesian-LightGBM model has an AUC of 0.835. From the importance of the factors, it can be seen that the average multi-year rainfall, elevation, POI kernel density and distance from the rivers are the most important factors for the occurrence of landslides, while the sand transport index, hydrodynamic index and slope position have a weaker effect on the occurrence of landslides. The hybrid model combining factor screening method-Bayesian-LightGBM can improve the accuracy of the model and provide a reference framework for constructing a rational factor database. By integrating the analysis with factor significance, it is verified that the geoprobe can accurately detect the contribution value of each factor to landslide occurrence, highlight the correlation between each combination of landslide conditioning factors, and thus explore the relationship between each factor and landslides.
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表 1 数据及数据来源
数据名称 数据来源 年份 类型 精度 多年平均降雨 地理国情监测云平台 2003-2019年 栅格 30 m DEM Global digital elevation model(GDEM) 2019年 栅格 30 m Landsat 8 美国地质调查局 2019年 栅格 30 m Landsat5 美国地质调查局 2019年 栅格 30 m 地质资料 国家地质资料数据中心 2019年 矢量 1:200 000 土地利用 地理国情监测云平台 2015年 矢量 1:100 000 行政区划 地理国情监测云平台 2019年 矢量 1:100 000 河网 中国科学院资源环境科学数据中心 2019年 矢量 1:100 000 道路 中国科学院资源环境科学数据中心 2019年 矢量 1:100 000 历史滑坡 重庆市地质监测站 2003-2019年 数据表 — 2016年POI 网络爬虫 2016年 矢量 1:100 000 
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表 2 滑坡因子分类表
因子分组 影响因子 分级 分类标准 地形地貌 高程/m 11 1) <530；2) 530~632；3) 632~723；4) 723~809；5) 809~895；6) ≥895~987；7) 987~1093；
8) 1093~ 1225；9) 1225~1390；10) 1390~1600；11) 1600~1953地形位置指数 10 1) <-13；2) -13~-7；3) -7~-4；4) -4~-1；5) -1~0；6) 0 ~2；7) 2~4；8) 4~8；9) 8~14；10) 14~58 起伏度/m 7 1) <20；2) 20~30；3) 30~40；4) 40~50；5) 50~80；6) 80~170；7) ≥170 坡度/° 8 1) <5；2) 5~10；3) 10~15；4) 15~20；5) 20~25；6) 25~30；7) 30~35；8) ≥35 坡向 9 1) 平面；2) 北；3) 东北；4) 东；5) 东南；6) 南；7) 西南；8)西；9) 西北 坡位 6 1) 谷底；2) 下坡；3) 平坡；4) 中坡；5) 山坡；6) 山脊 曲率 6 1) <-1；2) -1~-0.5；3) -0.5~0；4) 0~0.5；5) 0.5~1；6) ≥1 剖面曲率 6 1) <-1；2) -1~-0.5；3) -0.5~0；4) 0~0.5；5) 0.5~1；6) ≥1 平面曲率 6 1) <-1；2) -1~-0.5；3) -0.5~0；4) 0~0.5；5) 0.5~1；6) ≥1 微地貌 10 1) 峡谷/深流；2) 中坡处水系/浅谷；3) 高地水系/水源；4) U型山谷；5) 平原；6) 空旷斜坡；
7) 上斜坡/台地；8) 局部山谷中的山脊；9) 在平原中坡处山脊/小山；10) 山顶/山脊高处地表切割深度/m 6 1) <256；2) 256~545；3) 545~789；4) 789~1002；5) 1002~1197；6) 1197~1920 粗糙度指数 6 1) <1.05；2) 1.05~1.12；3) 1.12~1.24；4) 1.24~1.41；5) 1.41~1.72；6) 1.72~3.77 地质条件 岩性 9 1) є2-3；2) S2；3) S1；4) O1；5) Qb2b；6) Z；7) D；8) T1j；9) J1z-2x； 距断层距离/m 7 1) <2276；2) 2276~4815；3) 4815~7355；4) 7355~9894；5) 9894~12521；6) 12521~16111；
7) ≥16111环境条件 归一化植被指数 5 1) <0.5；2) 0.5~0.6；3) 0.6~0.7；4) 0.7~0.8；5) 0.8~0.9 距河流距离/m 7 1) <3508；2) 3508~7380；3) 7380~11251；4) 11251~15123；5) 15123~19115；6) 19115~23713；
7) ≥23713土地利用 9 1) 林地；2) 草地；3) 耕地；4) 园地；5) 住宅用地；6) 交通运输用地；7) 工矿仓储用地；8) 水域及水利设施用地；9) 其他用地 人类活动 距道路距离/m 7 1) <342；2) 342~755；3) 755~1243；4) 1243~1827；5) 1827~2581；6) 2581~3578；7) ≥3578 POI核密度 8 1) 4；2) 4~10；3) 10~26；4) 26~50；5) 50~77；6) 77~120；7) 120~170；8) 170~233 水文条件 多年平均降雨/mm 7 1) <1318；2) 1318~1347；3) 1347~1377；4) 1377~1409；5) 1409~1445；6) 1445~1489；
7) 1489~1551地形湿度指数 6 1) <4；2) 4~6；3) 6~8；4) 8~10；5) 10~13；6) 13~26 输沙指数 8 1) <20；2) 20~50；3) 50~100；4) 100~150；5) 100~200；6) 200~300；7) 300~400；8) 400~720 水流动力指数 8 1) <250；2) 250~1000；3) 1000~2000；4) 2000~3000；5) 3000~5000；6) 5000~10000；7) ≥10000
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表 3 地形地貌因子的皮尔逊相关系数
TRI 地形位置指数 高程 平面曲率 坡度 坡位 坡向 剖面曲率 起伏度 地表切割深度 曲率 微地貌 TRI 1 地形位置指数 0.663 1 高程 0.129 0.673 1 平均曲率 0.040 0.086 −0.025 1 坡度 0.874 0.654 0.073 0.055 1 坡位 0.003 0.066 0.066 0.237 −0.029 1 坡向 −0.028 −0.057 −0.02 0.003 −0.031 0.032 1 剖面曲率 0.114 0.023 −0.074 0.014 0.118 −0.291 0.310 1 起伏度 0.766 0.663 0.153 0.048 0.773 0.028 −0.017 0.110 1 地表切割深度 −0.340 0.115 0.624 0.002 −0.392 −0.015 0.079 −0.003 0.083 1 曲率 −0.014 0.014 0.041 0.195 −0.064 0.511 0.106 −0.309 0.007 0.065 1 微地貌 0.022 0.104 0.105 0.226 −0.021 0.632 0.070 −0.346 0.051 0.033 0.535 1
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表 4 其他因子的皮尔逊相关系数
地质条件 距断层距离 岩性 距断层距离 1 岩性 0.395 1 人类活动 POI核密度 距道路距离 POI核密度 1 距道路距离 −0.241 1 环境条件 NDVI 距河流距离 土地利用 NDVI 1 距河流距离 −0.157 1 土地利用 0.268 0.064 1 水文条件 SPI STI TWI 多年平均降雨 SPI 1 STI 0.476 1 TWI 0.158 0.270 1 多年平均降雨 0.020 0.066 −0.090 1
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表 6 3种模型的滑坡易发性区划分级统计
模型 易发性分级 面积/km2 滑坡个数/个 滑坡密度/(个/km2) 初始因子-Bayesian-LGB 极低易发区 1981.105 42 0.021 低易发区 780.323 101 0.129 中易发区 242.908 130 0.535 高易发区 99.845 115 1.152 极高易发区 53.963 157 2.909 PCC-Bayesian-LGB 极低易发区 2041.892 48 0.024 低易发区 716.984 105 0.146 中易发区 240.890 116 0.482 高易发区 107.440 125 1.163 极高易发区 50.962 151 2.963 GD-Bayesian-LGB 极低易发区 2213.294 51 0.023 低易发区 603.123 93 0.154 中易发区 199.891 103 0.515 高易发区 90.802 121 1.333 极高易发区 51.052 177 3.467
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表 7 3种模型滑坡易发性模型精度对比表
模型 准确率 精确率 召回率 F1分数 AUC测试集 AUC训练集 初始因子-LightGBM 0.908 0.808 0.908 0.898 0.801 0.949 PCC-LightGBM 0.922 0.828 0.922 0.922 0.824 0.981 GD-LightGBM 0.925 0.828 0.926 0.928 0.835 0.989
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