LIU Jian, LI Shulin, CHEN Tao. Landslide Susceptibility Assesment Based on Optimized Random Forest Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1085-1091. DOI: 10.13203/j.whugis20160515
Citation: LIU Jian, LI Shulin, CHEN Tao. Landslide Susceptibility Assesment Based on Optimized Random Forest Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1085-1091. DOI: 10.13203/j.whugis20160515

Landslide Susceptibility Assesment Based on Optimized Random Forest Model

Funds: 

The National High Technology Research and Development Program of China(863 Program) 2012AA121303

More Information
  • Author Bio:

    LIU Jian, PhD candidate, engineer, specializes in cloud computing and geological disaster assessment. E-mail:linefanliu@163.com

  • Corresponding author:

    LI Shulin, postgraduate. E-mail: lishulincug@gmail.com

  • Received Date: October 18, 2017
  • Published Date: July 04, 2018
  • The research area is located in Shazhenxi town and Xietan town of Three Gorges reservoir area in this paper. In order to obtain better results that discrete the continuous factors of landslide, entropy based on minimal description length principle(Ent-MDLP) method is used. To avoid the influence of correlation between factors, we calculate the Pearson correlation coefficient to remove high correlation factor. In order to obtain more accurate non-landslide sample points, the non-landslide sample points are randomly selected from the very low and low susceptible regions predicted by the entropy method. For the optimized random forests model, the optimal random features and its number are determined by iterative calculation of out-of-bag error estimation. Then the optimized random forest is evaluated for the landslide of the study area, and the landslide susceptibility level is divided. The model is compared with the methods of logistic regression, support vector machine and non-optimized random forest. The accuracy of each model is evaluated by plotting the receiver sensitivity curve of each algorithm. The optimized random forest's area is the highest, which the area under the curve is 91.8%. These show that the random forest model is optimized with more high-predictive power in landslide-prone assessment.
  • [1]
    刘阳. 延长县滑坡地质灾害风险评估和管理研究[D]. 西安: 长安大学, 2009

    Liu Yang. Extension of the County Landslide Disaster Risk Assessment and Management Research[D]. Xi'an: Chang'an University, 2009
    [2]
    许冲, 戴福初, 姚鑫, 等. GIS支持下基于层次分析法的汶川地震区滑坡易发性评价[J].岩石力学与工程学报, 2009, 28(a02):3978-3985 http://d.old.wanfangdata.com.cn/Periodical/yslxygcxb2009z2100

    Xu Chong, Dai Fuchu, Yao Xin, et al. GIS-Based Landslide Susceptibility Assessment Using Analytical Hierarchy Process in Wenchuan Earthquake Region[J]. Chinese Journal of Rock Mechanics and Engineering, 2009, 28(a02):3978-3985 http://d.old.wanfangdata.com.cn/Periodical/yslxygcxb2009z2100
    [3]
    罗向奎, 付旭辉.基于极限平衡法的杨家坝滑坡稳定性分析[J].山西建筑, 2009, 35(6):108-109 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=shanxjz200906066

    Luo Xiangkui, Fu Xuhui. Landslide Stability Ana-lysis of Yangjiaba Based Upon Limit Equilibrium Method[J].Shanxi Architecture, 2009, 35(6):108-109 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=shanxjz200906066
    [4]
    王卫东, 陈燕平, 钟晟.应用CF和Logistic回归模型编制滑坡危险性区划图[J].中南大学学报(自然科学版), 2009, 40(4):1127-1132 https://www.wenkuxiazai.com/doc/74f9d3462b160b4e767fcfb7-3.html

    Wang Weidong, Chen Yanping, Zhong Sheng. Landslides Susceptibility Mapped with CF and Logistic Regression Model[J].Journal of Central South University(Science and Technology), 2009, 40(4):1127-1132 https://www.wenkuxiazai.com/doc/74f9d3462b160b4e767fcfb7-3.html
    [5]
    王佳佳, 殷坤龙, 肖莉丽.基于GIS和信息量的滑坡灾害易发性评价——以三峡库区万州区为例[J].岩石力学与工程学报, 2014, 33(4):797-808 http://dqxxkx.cn/CN/abstract/abstract40303.shtml

    Wang Jiajia, Yin Kunlong, Xiao Lili. Landslide Susceptibility Assessment Based on GIS and Weighted Information Value:A Case Study of Wanzhou District, Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(4):797-808 http://dqxxkx.cn/CN/abstract/abstract40303.shtml
    [6]
    牛瑞卿, 彭令, 叶润青, 等.基于粗糙集的支持向量机滑坡易发性评价[J].吉林大学学报(地球科学版), 2012, 42(2):430-439 http://www.cqvip.com/QK/91256B/201202/41619273.html

    Niu Ruiqing, Peng Ling, Ye Runqing, et al. Landslide Susceptibility Assessment Based on Rough Sets and Support Vector Machine[J]. Journal of Jilin University(Earth Science Edition), 2012, 42(2):430-439 http://www.cqvip.com/QK/91256B/201202/41619273.html
    [7]
    武雪玲, 任福, 牛瑞卿, 等.斜坡单元支持下的滑坡易发性评价支持向量机模型[J].武汉大学学报·信息科学版, 2013, 38(12):1499-1503 http://www.cnki.com.cn/Article/CJFDTotal-YNSK201603015.htm

    Wu Xueling, Ren Fu, Niu Ruiqing, et al. Landslide Spatial Prediction Based on Slope Units and Support Vector Machines[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12):1499-1503 http://www.cnki.com.cn/Article/CJFDTotal-YNSK201603015.htm
    [8]
    Pradhan B. Manifestation of an Advanced Fuzzy Logic Model Coupled with Geo-information Techniques to Landslide Susceptibility Mapping and Their Comparison with Logistic Regression Modelling[J]. Environmental and Ecological Statistics, 2011, 18(3):471-493 doi: 10.1007/s10651-010-0147-7
    [9]
    曹正凤. 随机森林算法优化研究[D]. 北京: 首都经济贸易大学, 2014

    Cao Zhengfeng. Study on Optimization of Random Forests Algorithm[D]. Beijing: Capital University of Economics and Business, 2014
    [10]
    Breiman L. Random Forests[J]. Machine Lear-ning, 2001, 45(1):5-32 doi: 10.1023/A:1010933404324
    [11]
    方匡南, 吴见彬, 朱建平, 等.随机森林方法研究综述[J].统计与信息论坛, 2011, 26(3):32-38 http://www.cnki.com.cn/Article/CJFDTOTAL-TJLT201103007.htm

    Fang Kuangnan, Wu Jianbin, Zhu Jianping, et al. A Review of Technologies on Random Forests[J]. Statistics & Information Forum, 2011, 26(3):32-38 http://www.cnki.com.cn/Article/CJFDTOTAL-TJLT201103007.htm
    [12]
    李贞贵. 随机森林改进的若干研究[D]. 厦门: 厦门大学, 2013

    Li Zhengui. Several Research on Random Forest Improvement[D]. Xiamen: Xiamen University, 2013
    [13]
    董师师, 黄哲学.随机森林理论浅析[J].集成技术, 2013, 2(1):1-7 http://cdmd.cnki.com.cn/Article/CDMD-10559-1016734003.htm

    Dong Shishi, Huang Zhexue. A Brief Theoretical Overview of Random Forests[J].Journal of Integration Technology, 2013, 2(1):1-7 http://cdmd.cnki.com.cn/Article/CDMD-10559-1016734003.htm
    [14]
    安洲. 基于随机森林的硬盘故障预测算法的研究[D]. 天津: 南开大学, 2014

    An Zhou. Hard Drive Failure Prediction Based on Random Forest[D]. Tianjin: Nankai University, 2014
    [15]
    彭令. 三峡库区滑坡灾害风险评估研究[D]. 武汉: 中国地质大学, 2013

    Peng Ling. Landslide Risk Assessment in the Three Gorges Reservoir[D]. Wuhan: China University of Geosciences, 2013
    [16]
    田正国, 程温鸣, 卢书强, 等.三峡库区滑坡崩塌发育的控制与诱发因素分析[J].资源环境与工程, 2013, 27(1):50-55 http://www.cqvip.com/QK/82916A/201301/47948282.html

    Tian Zhengguo, Cheng Wenming, Lu Shuqiang, et al. Control and Triggering Factors Analysis of Landslides and Rockfalls in the Three Gorges Re-servoir Area[J]. Resources Environment & Engineering, 2013, 27(1):50-55 http://www.cqvip.com/QK/82916A/201301/47948282.html
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