• 中国出版政府奖提名奖

    中国百强科技报刊

    湖北出版政府奖

    中国高校百佳科技期刊

    中国最美期刊

    留言板

    尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

    姓名
    邮箱
    手机号码
    标题
    留言内容
    验证码

    基于随机森林的滑坡空间易发性评价:以三峡库区湖北段为例

    吴润泽 胡旭东 梅红波 贺金勇 杨建英

    吴润泽, 胡旭东, 梅红波, 贺金勇, 杨建英, 2021. 基于随机森林的滑坡空间易发性评价:以三峡库区湖北段为例. 地球科学, 46(1): 321-330. doi: 10.3799/dqkx.2020.032
    引用本文: 吴润泽, 胡旭东, 梅红波, 贺金勇, 杨建英, 2021. 基于随机森林的滑坡空间易发性评价:以三峡库区湖北段为例. 地球科学, 46(1): 321-330. doi: 10.3799/dqkx.2020.032
    Wu Runze, Hu Xudong, Mei Hongbo, He Jinyong, Yang Jianying, 2021. Spatial Susceptibility Assessment of Landslides Based on Random Forest: A Case Study from Hubei Section in the Three Gorges Reservoir Area. Earth Science, 46(1): 321-330. doi: 10.3799/dqkx.2020.032
    Citation: Wu Runze, Hu Xudong, Mei Hongbo, He Jinyong, Yang Jianying, 2021. Spatial Susceptibility Assessment of Landslides Based on Random Forest: A Case Study from Hubei Section in the Three Gorges Reservoir Area. Earth Science, 46(1): 321-330. doi: 10.3799/dqkx.2020.032

    基于随机森林的滑坡空间易发性评价:以三峡库区湖北段为例

    doi: 10.3799/dqkx.2020.032
    基金项目: 

    三峡库区后续地质灾害防治信息系统建设 0001212012AC50001

    详细信息
      作者简介:

      吴润泽(1979-), 男, 工程师, 主要从事地质灾害防治信息化研究.ORCID:0000-0003-1063-1431.E-mail:wurunze@163.com

      通讯作者:

      梅红波, ORCID:0000-0001-6377-3877.E-mail:hbmei@cug.edu.cn

    • 中图分类号: P642.2

    Spatial Susceptibility Assessment of Landslides Based on Random Forest: A Case Study from Hubei Section in the Three Gorges Reservoir Area

    • 摘要: 滑坡空间易发性分析有助于开展滑坡防灾减灾工作,训练有效的滑坡预测模型在其中扮演重要角色.以三峡库区湖北段为研究区,选取高程、坡度、斜坡结构、土地利用类型、岩土体类型、断裂距离、路网距离、河网距离、以及归一化植被指数这9个影响因子建立滑坡空间数据库,采用集成学习中的随机森林算法进行滑坡易发性评价.结果显示,随机森林抽样训练的方式有利于确定较优的训练参数,保证随机森林在不过拟合的情况下取得满意的拟合能力和泛化能力.随机森林绘制的滑坡易发性分级图显示出合理的空间分布,其中73.35%的滑坡分布在较高和极高级别区域.而巴东县北部、秭归县中部以及夷陵区南部等区域显示出较高的易发性级别.性能评估及易发性统计结果均表明随机森林是一种出色的算法,在滑坡空间预测领域具有较好的适用性.

       

    • 图  1  研究区地理位置以及滑坡分布

      Fig.  1.  Locations of study area and landslides

      图  2  随机森林OOB误差曲线图

      Fig.  2.  OOB error curves of random forest

      a. mtry=2;b. mtry=3;c. mtry=4;d. mtry=5

      图  3  随机森林ROC曲线

      Fig.  3.  The ROC curve of random fores

      图  4  研究区滑坡易发性评价图

      Fig.  4.  Landslide susceptibility maps in the study area

      表  1  评价因子汇总表

      Table  1.   The summary of conditioning factors

      高程(m) 坡度(°) 斜坡结构
      类别 PA(%) PL(%) FR 类别 PA(%) PL(%) FR 类别 PA(%) PL(%) FR
      ≤200 7.44 10.22 1.37 ≤5 6.22 2.47 0.40 水平坡 0.40 0.52 1.30
      200~400 9.21 30.96 3.36 5~10 11.05 6.49 0.59 顺向坡 18.33 18.50 1.01
      400~600 11.84 20.05 1.69 10~15 15.21 14.59 0.96 顺斜向坡 17.83 18.27 1.02
      600~800 13.75 14.13 1.03 15~20 17.34 22.11 1.28 横向坡 32.79 33.20 1.01
      800~1000 15.28 11.72 0.77 20~25 16.15 23.09 1.43 逆斜向坡 15.56 14.47 0.93
      1 000~1 200 15.60 7.01 0.45 25~30 12.69 16.94 1.34 逆向坡 15.09 15.05 1.00
      1 200~1 400 13.37 3.73 0.28 30~35 9.04 8.50 0.94
      > 1 400 13.51 2.18 0.16 35~40 5.88 3.10 0.53
      > 40 6.43 2.70 0.42
      路网距离(m) NDVI 断裂距离(m)
      类别 PA(%) PL(%) FR 类别 PA(%) PL(%) FR 类别 PA(%) PL(%) FR
      ≤50 15.99 30.96 1.94 ≤-0.1 2.12 3.27 1.54 ≤1000 19.75 17.92 0.91
      50~100 12.52 21.65 1.73 -0.1~0 1.85 2.81 1.52 1 000~2 000 16.07 13.10 0.81
      100~200 17.96 22.63 1.26 0~0.1 4.53 6.66 1.47 2 000~3 000 12.68 10.45 0.82
      200~300 11.97 9.88 0.83 0.1~0.2 10.27 11.60 1.13 3 000~4 000 9.63 8.62 0.89
      300~400 8.36 5.05 0.60 0.2~0.3 19.00 17.69 0.93 4 000~5 000 7.74 6.72 0.87
      400~500 6.11 3.22 0.53 0.3~0.4 27.02 23.95 0.89 5 000~6 000 6.59 8.27 1.26
      500~600 4.66 1.84 0.39 0.4~0.5 25.24 23.43 0.93 6 000~7 000 5.71 9.25 1.62
      600~700 3.60 1.03 0.29 > 0.5 9.96 10.57 1.06 7 000~8 000 4.85 5.51 1.14
      700~800 2.91 1.21 0.41 8 000~9 000 4.10 4.77 1.16
      > 800 15.91 2.53 0.16 > 9 000 12.88 15.39 1.19
      河网距离(m) 土地利用类型 岩土体类型
      类别 PA(%) PL(%) FR 类别 PA(%) PL(%) FR 类别 PA(%) PL(%) FR
      ≤500 17.34 21.83 1.26 农地 14.38 17.98 1.25 松散岩土类 12.09 24.99 2.07
      500~1 000 17.04 18.90 1.11 园地 6.68 22.80 3.41 碎屑岩类 21.63 38.94 1.80
      1 000~1 500 15.72 14.88 0.95 居民地 2.97 8.90 3.00 碳酸盐岩类 52.40 29.47 0.56
      1 500~2 000 13.33 9.13 0.69 林地 72.47 43.54 0.60 岩浆岩及变质岩类 13.88 6.61 0.48
      2 000~2 500 10.30 8.39 0.81 草地 0.56 0.52 0.93
      2 500~3 000 7.79 6.32 0.81 其它 2.95 6.26 2.12
      3 000~3 500 5.65 6.84 1.21
      > 3 500 12.84 13.73 1.07
      下载: 导出CSV

      表  2  随机森林性能统计表

      Table  2.   Performance of random forest

      性能度量指标 准确度 均方根误差 kappa系数
      训练集 0.791 0.390 0.582
      测试集 0.753 0.417 0.507
      下载: 导出CSV

      表  3  易发性评价灾害点分布统计表

      Table  3.   The summary of landslide locations in corresponding susceptibility classes

      易发性等级 滑坡数 滑坡百分比(%) 栅格数 栅格百分比(%) 滑坡密度
      极高 1 040 58.47 1 839 800 14.57 4.01
      较高 265 14.88 1 475 600 11.69 1.27
      中等 218 12.23 1 695 650 13.43 0.91
      较低 125 7.00 2 232 150 17.68 0.40
      极低 132 7.41 5 382 825 42.63 0.17
      下载: 导出CSV
    • Aditian, A., Kubota, T., Shinohara, Y., 2018. Comparison of GIS-Based Landslide Susceptibility Models Using Frequency Ratio, Logistic Regression, and Artificial Neural Network in a Tertiary Region of Ambon, Indonesia. Geomorphology, 318:101-111. https://doi.org/10.1016/j.geomorph.2018.06.006
      Alcántara-Ayala, I., 2002. Geomorphology, Natural Hazards, Vulnerability and Prevention of Natural Disasters in Developing Countries. Geomorphology, 47(2/3/4):107-124. https://doi.org/10.1016/s0169-555x(02)00083-1
      Bennett, G. L., Miller, S. R., Roering, J. J., et al., 2016. Landslides, Threshold Slopes, and the Survival of Relict Terrain in the Wake of the Mendocino Triple Junction. Geology, 44(5):363-366. https://doi.org/10.1130/g37530.1
      Breiman, L., Friedman, J. H., Olshen, R. A., et al., 1984. Classification and Regression Trees. Chapman & Hall, New York.
      Breiman, L., 2001. Random Forests. Machine Learning, 45(1):5-32. https://doi.org/10.1023/A:1010933404324.
      Feng, H.J., Zhou, A.G., Yu, J.J., et al., 2016. A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province. Earth Science, 41(3):403-415 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-DQKX201603006.htm
      Felicísimo, Á. M., Cuartero, A., Remondo, J., et al., 2013. Mapping Landslide Susceptibility with Logistic Regression, Multiple Adaptive Regression Splines, Classification and Regression Trees, and Maximum Entropy Methods:A Comparative Study. Landslides, 10(2):175-189. https://doi.org/10.1007/s10346-012-0320-1
      Fenti, V., Silvano, S., Spagna, V., 1979. Methodological Proposal for an Engineering Geomorphological Map. Forecasting Rockfalls in the Alps. Bulletin of the International Association of Engineering Geology, 19(1):134-138. https://doi.org/10.1007/bf02600465
      Guo, H.J., Tang, N.Q., Lin, J.B., 2010. Sensibility Analysis of Land-Use and Landslide Hazard Based on GIS in Xianyou County. Journal of Fujian Agriculture and Forestry University (Natural Science Edition), 39(4):417-420 (in Chinese with English abstract). http://www.cabdirect.org/abstracts/20103284954.html;jsessionid=4FA7BBD33F5E271F97960BEF557B26B0
      Guo, Z.Z., Yin, K.L., Fu, S., et al., 2018. Evaluation of Landslide Susceptibility Based on GIS and WOE-BP Model. Earth Science, 44(12):4299-4312 (in Chinese with English abstract). http://www.researchgate.net/publication/324390254_Evaluation_of_Landslide_Susceptibility_Based_on_GIS_and_WOE-BP_Model
      Guo, Z.Z., Yin, K.L., Liu, Q.L., et al., 2019. Rainfall Warning of Creeping Landslide in Yunyang County of Three Gorges Reservoir Region Based on Displacement Ratio Model. Earth Science, 45(2):672-684 (in Chinese with English abstract).
      Jiang, R., 2010. Research on Generation Mechanism and Service of Slope Disaster Warning Information in Kunming City(Dissertation). Kunming University of Science and Technology, Kunming (in Chinese with English abstract).
      Kornejady, A., Ownegh, M., Bahremand, A., 2017. Landslide Susceptibility Assessment Using Maximum Entropy Model with Two Different Data Sampling Methods. CATENA, 152:144-162. https://doi.org/10.1016/j.catena.2017.01.010
      Li, S.L., Xu, Q., Tang, M.G., et al., 2018. Study on Spatial Distribution and Key Influencing Factors of Landslides in Three Gorges Reservoir Area. Earth Science, 45(1):341-354 (in Chinese with English abstract).
      Li, Y.Y., Mei, H.B., Ren, X.J., et al., 2018.Geological Disaster Susceptibility Evaluation Based on Certainty Factor and Support Vector Machine. Journal of Geo-Information Science, 20(12):1699-1709 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQXX201812003.htm
      Liu, L.N., Xu, C., Xu, X.W., et al., 2014. GIS-Based Landslide Hazard Evaluation Using AHP Method in the 2013 Lushan Earthquake Region. Journal of Catastrophology, 29(4):183-191 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-ZHXU201404037.htm
      Nie, J., Lian, J., Hu, Z.W., 2014. Spatial Variation of Landslides in Wenchuan Earthquake-Stricken Areas. Geographical Research, 33 (2):214-224 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLYJ201402003.htm
      Shi, X.G., Xu, J.H., Jiang, H.J., et al., 2019. Slope Stability State Monitoring and Updating of the Outang Landslide, Three Gorges Area with Time Series InSAR Analysis. Earth Science, 44(12):4284-4292 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201912038.htm
      Pham, B. T., Pradhan, B., Tien Bui, D., et al., 2016. A Comparative Study of Different Machine Learning Methods for Landslide Susceptibility Assessment:A Case Study of Uttarakhand Area (India). Environmental Modelling & Software, 84:240-250. https://doi.org/10.1016/j.envsoft.2016.07.005
      Song, Y. Q., Gong, J. H., Gao, S., et al., 2012. Susceptibility Assessment of Earthquake-Induced Landslides Using Bayesian Network:A Case Study in Beichuan, China. Computers & Geosciences, 42:189-199. https://doi.org/10.1016/j.cageo.2011.09.011
      Tehrany, M. S., Pradhan, B., Jebur, M. N., 2013. Spatial Prediction of Flood Susceptible Areas Using Rule Based Decision Tree (DT) and a Novel Ensemble Bivariate and Multivariate Statistical Models in GIS. Journal of Hydrology, 504:69-79. https://doi.org/10.1016/j.jhydrol.2013.09.034
      Wang, L.L., 2016. Feature Processing Method in Rainfall-induced Landslide Susceptibility Assessment (Dissertation). Zhejiang University, Hangzhou (in Chinese with English abstract).
      Wu, S., Li, L.W., Li, S.J., 2012. Analysis on the Influence of Coupling Relationship between Rock Mass Structural Plane and Water on Slope Stability. Journal of Jingchu University of Technology, 27(2):33-36 (in Chinese with English abstract).
      Wu, X.L., Shen, S.Q., Niu, R.Q., 2016. Landslide Susceptibility Prediction Using GIS and PSO-SVM. Geomatics and Information Science of Wuhan University, 41(5):665-671 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-WHCH201605015.htm
      Xiang, L.Z., Cui, P., 2014. Triggering Factors Susceptibility of Earthquake-Induced Collapses and Landslides in Wenchuan County. Journal of Sichuan University (Engineering Science Edition), 42(5):105-112 (in Chinese with English abstract). http://www.researchgate.net/publication/289748349_Triggering_factors_susceptibility_of_earthquake-induced_collapses_and_landslides_in_Wenchuan_County
      Xiao T., 2007. GIS-Based Research on Spatial Simulation of Landslide in Lanzhou City (Dissertation). Lanzhou University, Lanzhou (in Chinese with English abstract).
      Zare, M., Pourghasemi, H. R., Vafakhah, M., et al., 2013. Landslide Susceptibility Mapping at Vaz Watershed (Iran) Using an Artificial Neural Network Model:A Comparison between Multilayer Perceptron (MLP) and Radial Basic Function (RBF) Algorithms. Arabian Journal of Geosciences, 6(8):2873-2888. https://doi.org/10.1007/s12517-012-0610-x
      冯杭建, 周爱国, 俞剑君, 等, 2016.浙西梅雨滑坡易发性评价模型对比.地球科学, 41(3):403-415. doi: 10.3799/dqkx.2016.032
      郭慧娟, 唐南奇, 林金宝, 2010.基于GIS的仙游县土地利用与滑坡灾害敏感性分析.福建农林大学学报(自然科学版), 39(4):417-420. https://www.cnki.com.cn/Article/CJFDTOTAL-FJND201004018.htm
      郭子正, 殷坤龙, 付圣, 等, 2018.基于GIS与WOE-BP模型的滑坡易发性评价.地球科学, 44(12):4299-4312. doi: 10.3799/dqkx.2016.032
      郭子正, 殷坤龙, 刘庆丽, 等, 2019.基于位移比模型的三峡库区云阳县域内蠕变型滑坡降雨预警.地球科学45(2):672-684. doi: 10.3799/dqkx.2019.005
      蒋锐, 2010.昆明市斜坡灾害预警信息生成机理及服务研究(博士学位论文).昆明: 昆明理工大学.
      李松林, 许强, 汤明高, 等, 2018.三峡库区滑坡空间发育规律及其关键影响因子.地球科学, 45(1):341-354. doi: 10.3799/dqkx.2017.576
      李远远, 梅红波, 任晓杰, 等, 2018.基于确定性系数和支持向量机的地质灾害易发性评价.地球信息科学学报, 20(12):1699-1709. doi: 10.12082/dqxxkx.2018.180349
      刘丽娜, 许冲, 徐锡伟, 等, 2014. GIS支持下基于AHP方法的2013年芦山地震区滑坡危险性评价.灾害学, 29(4):183-191. doi: 10.3969/j.issn.1000-811X.2014.04.034
      聂娟, 连健, 胡卓玮, 2014.汶川地震灾区滑坡空间特征变化分析.地理研究, 33(2):214-224. https://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ201402003.htm
      史绪国, 徐金虎, 蒋厚军, 等, 2019.时序InSAR技术三峡库区藕塘滑坡稳定性监测与状态更新.地球科学, 44(12):4284-4292. doi: 10.3799/dqkx.2019.180
      王丽丽, 2016.降雨型滑坡地质灾害易发性评价中的特征处理方法.杭州: 浙江大学.
      武尚, 李利文, 李世佳, 2012.岩体结构面与水耦合关系对边坡稳定性的影响分析.荆楚理工学院学报, 27(2):33-36 doi: 10.3969/j.issn.1008-4657.2012.02.007
      武雪玲, 沈少青, 牛瑞卿, 2016. GIS支持下应用PSO-SVM模型预测滑坡易发性.武汉大学学报(信息科学版), 41(5):665-671. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201605015.htm
      向灵芝, 崔鹏, 2014.汶川县地震诱发崩滑灾害影响因素的敏感性分析.四川大学学报(工程科学版), 42(5):105-112. https://www.cnki.com.cn/Article/CJFDTOTAL-SCLH201005016.htm
      肖桐, 2007.基于GIS的兰州市滑坡空间模拟研究(硕士学位论文).兰州: 兰州大学.
    • 加载中
    图(4) / 表(3)
    计量
    • 文章访问数:  2038
    • HTML全文浏览量:  1334
    • PDF下载量:  134
    • 被引次数: 0
    出版历程
    • 收稿日期:  2020-02-21
    • 刊出日期:  2021-01-15

    目录

      /

      返回文章
      返回