Xi'an Center, China Geological SurveyHost
Advanced Search
Previous Article Next Article
2024 Vol. 57, No. 1
Article Contents
Article navigation > Northwestern Geology > 2024 > 57(1): 34-43
Next Article Previous Article

WANG Bendong, LI Siquan, XU Wanzhong, YANG Yong, LI Yongyun. 2024. A Comparative Study of Landslide Susceptibility Evaluation Based on Three Different Machine Learning Algorithms. Northwestern Geology, 57(1): 34-43. doi: 10.12401/j.nwg.2023033
Citation: WANG Bendong, LI Siquan, XU Wanzhong, YANG Yong, LI Yongyun. 2024. A Comparative Study of Landslide Susceptibility Evaluation Based on Three Different Machine Learning Algorithms. Northwestern Geology, 57(1): 34-43. doi: 10.12401/j.nwg.2023033
shu

A Comparative Study of Landslide Susceptibility Evaluation Based on Three Different Machine Learning Algorithms

  • 1.

    Panzhihua City Natural Resources and Planning Bureau, Panzhihua 617000, Sichuan, China

  • 2.

    Southwest Nonferrous Kunming Exploration Surveying and Designing (Institute) Inc, Kunming 650051, Yunnan, China

  • 3.

    Faculty of Land and ResourcesEngineering, Kunming University of Science and Technology, Kunming 650093, Yunnan, China

More Information
Received Date:  12 October 2022
Revised Date:  03 November 2023
Publish Date:  20 February 2024
    Abstract

  • Accurate landslide susceptibility evaluation results are the key to landslide disaster prevention and control in mountainous areas, which can effectively avoid the risk caused by potential landslides. To obtain an accurate and reliable reference for landslide prevention, this paper selects nine evaluation factors, including elevation, stratigraphic lithology, average annual rainfall et al, and constructs a landslide susceptibility evaluation index system in the study area through multiple covariance analysis, taking Mangcheng, Yunnan Province as the research object. Subsequently, three typical machine learning models based on support vector machine (SVM), BP neural network and random forest (RF) were used for landslide susceptibility evaluation. Finally, the accuracy of the model evaluation results was compared and validated by using accuracy (ACC), area under the ROC curve (AUC), landslide ratio (Sei) and field fieldwork. The results showed that the RF model had the highest SeV values of 0.867, 0.94 and 9.21 for ACC, AUC, and very high susceptibility areas, respectively; the BP neural network model had the second highest values of 0.829, 0.90 and 9.14; the SVM had the lowest values of 0.794, 0.88 and 6.85; and the RF model results were more consistent with the field study. The results of experiments show that compared with the other two algorithms, the RF algorithm has higher accuracy and reliability in the Mangshi region and is more suitable for landslide susceptibility modeling in the region, and the evaluation results obtained by using the model can provide a theoretical basis and scientific reference for landslide control in the Mangshi region.

    • FullText(HTML)
  • 加载中
    • References
  • [1] 陈玉萍, 袁志强, 周博, 等. 遗传算法优化BP网络在滑坡灾害预测中的应用研究[J]. 水文地质工程地质, 2012, 39(1): 114-119

    Google Scholar

    Chen Yuping, Yuan Zhiqiang, Zhou Bo, et al. Application of back propagation neural networks with optimization of genetic algorithms to landslide hazard prediction[J]. Hydrogeology & Engineering Geology, 2012, 39(1): 114-119. (in Chinese)

    Google Scholar

    [2] 杜国梁, 杨志华, 袁颖, 等. 基于逻辑回归–信息量的川藏交通廊道滑坡易发性评价[J]. 水文地质工程地质, 2021, 48(5): 102-111

    Google Scholar

    Du Guoliang, Yang Zhihua, Yuan Ying, et al. Landslide susceptibility mapping in the Sichuan-Tibet trafficcorridor using logistic regression- information value method[J]. Hydrogeology & Engineering Geology, 2021, 48(5): 102-111. (in Chinese)

    Google Scholar

    [3] 黄发明, 胡松雁, 闫学涯, 等. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J]. 地质科技通报, 2022, 41(2): 79–90

    Google Scholar

    HUANG Faming, HU Songyan, YAN Xueya, et al. Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models[J]. Bulletin of Geological Science and Technology 2022, 41(2): 79–90.

    Google Scholar

    [4] 黄武彪, 丁明涛, 王栋, 等. 基于层数自适应加权卷积神经网络的川藏交通廊道沿线滑坡易发性评价[J]. 地球科学, 2022, 47(6): 2015-2030

    Google Scholar

    Huang Wubiao, Ding Mingtao, Wang Dong, et al. Evaluation of landslide susceptibility based on layer adaptive weighted convolutional neural network model along Sichuan-tibet traffic torridor[J]. Earth Science, 2022, 47(6): 2015-2030. (in Chinese)

    Google Scholar

    [5] 黄煜, 谢婉丽, 刘琦琦, 等. 基于GIS与MaxEnt模型的滑坡易发性评价—以铜川市中部城区为例[J]. 西北地质, 2023, 56(1): 266-275.

    Google Scholar

    HUANG Yu, XIE Wanli, LIU Qiqi, et al. Landslide Susceptibility Assessment Based on GIS and MaxEnt Model: Example from Central Districts in Tongchuan City[J]. Northwestern Geology, 2023, 56(1): 266−275.

    Google Scholar

    [6] 贾俊, 毛伊敏, 孟晓捷, 等. 深度随机森林和随机森林算法的滑坡易发性评价对比—以汉中市略阳县为例[J]. 西北地质, 2023, 56(3): 239−249.

    Google Scholar

    JIA Jun, MAO Yimin, MENG Xiaojie, et al. Comparison of Landslide Susceptibility Evaluation by Deep Random Forest and Random Forest Model: A Case Study of Lueyang County, Hanzhong City[J]. Northwestern Geology, 2023, 56(3): 239−249.

    Google Scholar

    [7] 康孟羽, 朱月琴, 陈晨, 等. 基于多元非线性回归和BP神经网络的滑坡滑动距离预测模型研究[J]. 地质通报, 2022, 41(12): 2281−2289.

    Google Scholar

    KANG Mengyu, ZHU Yueqin, CHEN Chen, et al. Research on landslide sliding distance prediction model based on multiple nonlinear regression and BP neural network[J]. Geological Bulletin of China, 2022, 41(12): 2281−2289.

    Google Scholar

    [8] 李东, 周可法, 孙卫东, 等. BP神经网络和SVM在矿山环境评价中的应用分析[J]. 干旱区地理, 2015, 38(1): 128-134

    Google Scholar

    Li Dong, Zhou Kefa, Sun WeiDong, et al. Application of BP neural network and SVM in mine environmental assessment[J]. Arid Land Geography, 2015, 38(1): 128-134. (in Chinese)

    Google Scholar

    [9] 李宇嘉, 陈宁生, 杨溢, 等. 汉源县中海村“8·21”雨后型滑坡特征与成因[J]. 成都理工大学学报(自然科学版), 2022, 49(2): 185−195.

    Google Scholar

    LI Ningjia, CHENG Ningsheng, YANG Yi, et al. Characteristics and causes of 8·21 rainfall-induced landslide at Zhonghai Village, Hanyuan County, Sichuan, China[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2022, 49(2): 185−195.

    Google Scholar

    [10] 林荣福, 刘纪平, 徐胜华, 等. 随机森林赋权信息量的滑坡易发性评价方法[J]. 测绘科学, 2020, 45(12): 131-138

    Google Scholar

    Lin Rongfu, Liu Jiping, Xu Shenghua, et al. Evaluation method of landslide susceptibility based on random forest weighted information[J]. Science of Surveying and Mapping, 2020, 45(12): 131-138. (in Chinese)

    Google Scholar

    [11] 刘坚, 李树林, 陈涛. 基于优化随机森林模型的滑坡易发性评价[J]. 武汉大学学报(信息科学版), 2018, 43(7): 1085-1091

    Google Scholar

    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. (in Chinese)

    Google Scholar

    [12] 刘璐瑶, 高惠瑛, 李照. 基于CF与Logistic回归模型耦合的永嘉县滑坡易发性评价[J]. 中国海洋大学学报(自然科学版), 2021, 51(10): 121-129

    Google Scholar

    Liu Luyao, Gao Huiying, Li Zhao. Landslide susceptibility assessment based on coupling of CF model and logistic regression model in Yongjia county[J]. Periodical of Ocean University of China, 2021, 51(10): 121-129. (in Chinese)

    Google Scholar

    [13] 刘希林, 庙成, 田春山. 区域滑坡和泥石流灾害两种危险性评价方法的比较分析[J]. 防灾减灾工程学报, 2017, 37(1): 71-78

    Google Scholar

    Liu Xilin, Miao Cheng, Tian Chunshan. Comparative analysis of two methods for assessing hazard of landslide and debris-flow on a regional scale[J]. Journal of Disaster Prevention and Mitigation Engineering, 2017, 37(1): 71-78. (in Chinese)

    Google Scholar

    [14] 马啸, 王念秦, 李晓抗, 等. 基于RF-FR模型的滑坡易发性评价——以略阳县为例[J]. 西北地质, 2022, 55(3): 335−344.

    Google Scholar

    MA Xiao, WANG Nianqin, LI Xiaokang, YAN Dong, LI Jialin. Assessment of Landslide Susceptibility Based on RF-FR Model: Taking Lueyang County as an Example[J]. Northwestern Geology, 2022, 55(3): 335−344.

    Google Scholar

    [15] 孟晓捷, 张新社, 曾庆铭, 等. 基于加权信息量法的黄土滑坡易发性评价——以1: 5万天水市麦积幅为例[J]. 西北地质, 2022, 55(2): 249−259.

    Google Scholar

    MENG Xiaojie, ZHANG Xinshe, ZENG Qingming, et al. The Susceptibility Evaluation of Loess Landslide Based on Weighted Information Value Method: Taking 1: 50 000 Map of Maiji District of Tianshui City As an Example[J]. Northwestern Geology, 2022, 55(2): 249−259.

    Google Scholar

    [16] 唐睿旋, 晏鄂川, 唐薇. 基于粗糙集和BP神经网络的滑坡易发性评价[J]. 煤田地质与勘探, 2017, 45(6): 129-138

    Google Scholar

    Tan Ruixuan, Yan Echuan, Tan Wei. Landslide susceptibility evaluation based on rough set and back-propagation neural network[J]. Coal Geology & Exploration, 2017, 45(6): 129-138. (in Chinese)

    Google Scholar

    [17] 孙萍萍, 张茂省, 贾俊, 等. 中国西部黄土区地质灾害调查研究进展[J]. 西北地质, 2022, 55(3): 96−107.

    Google Scholar

    SUN Pingping, ZHANG Maosheng, JIA Jun, et al. Geo-hazards Research and Investigation in the Loess Regions of Western China[J]. Northwestern Geology, 2022, 55(3): 96−107.

    Google Scholar

    [18] 田媛, 巨能攀, 解明礼, 等. 滑坡编录表达模式对易发性评价结果的影响[J]. 成都理工大学学报(自然科学版), 2022, 49(5): 606−615.

    Google Scholar

    TIAN Yuan, JU Nengpan, XIE Mingli, et al. Analysis of the impact of landslide cataloguing expression patterns on the evaluation results of landslide susceptibility[J], Journal of Chengdu University of Technology (Science Technology Edition), 2022, 49(5): 606−615.

    Google Scholar

    [19] 王朋伟, 安玉科. 滑坡匀速变形阶段快速诊断方法研究[J]. 西北地质, 2023, 56(5): 197−203.

    Google Scholar

    WANG Pengwei, AN Yuke. Research on Rapid Diagnosis Method of Landslide’s Uniform Deformation Stage[J]. Northwestern Geology, 2023, 56(5): 197−203.

    Google Scholar

    [20] 王海芝, 王颂, 周剑, 等. 樟木堆积体斜坡动力稳定性与极限承载力评价[J]. 西北地质, 2022, 55(1): 262−273.

    Google Scholar

    WANG Haizhi, WANG Song, ZHOU Jian, et al. Dynamic Stability Analysis and Ultimate Bearing Capacity Evaluation of Zhangmu Landslide Deposit[J]. Northwestern Geology, 2022, 55(1): 262−273.

    Google Scholar

    [21] 王毅, 方志策, 牛瑞卿, 等. 基于深度学习的滑坡灾害易发性分析[J]. 地球信息科学学报, 2021, 23(12): 2244-2260 doi: 10.12082/dqxxkx.2021.210057

    CrossRef Google Scholar

    Wang Yi, Fang Zhice, Niu Ruiqing, et al. Landslide susceptibility analysis based on deep learning[J]. Journal of Geo-information Science, 2021, 23(12): 2244-2260. (in Chinese) doi: 10.12082/dqxxkx.2021.210057

    CrossRef Google Scholar

    [22] 吴孝情, 赖成光, 陈晓宏, 等. 基于随机森林权重的滑坡危险性评价: 以东江流域为例[J]. 自然灾害学报, 2017, 26(5): 119-129

    Google Scholar

    Wu Xiaoqing, Lai Chengguang, Chen Xiaohong, et al. A landslide hazard assessment based on random forest weight: a case study in the Dongjiang River Basin[J]. Journal of Natural Disasters, 2017, 26(5): 119-129. (in Chinese)

    Google Scholar

    [23] 杨平芬, 李艳美, 杨保住. 云南省芒市土壤肥力现状及变化趋势[J]. 云南农业科技, 2014, (5): 4-7 doi: 10.3969/j.issn.1000-0488.2014.05.001

    CrossRef Google Scholar

    Yang Pingfen, Li Yanmei, Yang Baozhu. Status and trends of soil fertility in Mangshi, Yunnan province[J]. Yunnan Agricultural Science and Technology, 2014, (5): 4-7. (in Chinese) doi: 10.3969/j.issn.1000-0488.2014.05.001

    CrossRef Google Scholar

    [24] 张林梵, 王佳运, 张茂省, 等. 基于BP神经网络的区域滑坡易发性评价[J]. 西北地质, 2022, 55(2): 260-270

    Google Scholar

    Zhang Linfan, Wang Jiayun, Zhang Maosheng, et al. Evaluation of regional landslide susceptibility assessment based on BP neural network[J]. Northwestern Geology, 2022, 55(2): 260-270. (in Chinese)

    Google Scholar

    [25] 郑迎凯, 陈建国, 王成彬, 等. 确定性系数与随机森林模型在云南芒市滑坡易发性评价中的应用[J]. 地质科技通报, 2020, 39(6): 131-144

    Google Scholar

    Zheng Yingkai, Chen Jianguo, Wang Chengbin, et al. Application of certainty factor and random forests model in landslide susceptibility evaluation in Mangshi city[J]. Bulletin of Geological Science and Technology, 2020, 39(6): 131-144. (in Chinese)

    Google Scholar

    [26] 周定义, 左小清, 喜文飞, 等. 联合SBAS-InSAR和PSO-BP算法的高山峡谷区地质灾害危险性评价[J]. 农业工程学报, 2021, 37(23): 108-116 doi: 10.11975/j.issn.1002-6819.2021.23.013

    CrossRef Google Scholar

    Zhou Dingyi, Zuo Xiaoqing, Xi Wenfei, et al. Combined SBAS-InSAR and PSO-BP algorithm for evaluating the risk of geological disasters in alpine valley regions[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(23): 108-116. (in Chinese) doi: 10.11975/j.issn.1002-6819.2021.23.013

    CrossRef Google Scholar

    [27] 周宇, 常鸣, 孙文静, 等. 基于改进证据权重法的北海道地震同震滑坡易发性评价[J]. 地理与地理信息科学, 2022, 38(1): 138-144

    Google Scholar

    Zhou Yu, Chang Ming, Sun Wenjing, et al. Susceptibility evaluation of Hokkaido earthquake coseismic landslides based on improved weights of evidence method[J]. Geography and Geo-Information Science, 2022, 38(1): 138-144. (in Chinese)

    Google Scholar

    [28] Adnan Ozdemir, Tolga Altural. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey[J]. Journal of Asian Earth Sciences, 2013, 64: 180-197. doi: 10.1016/j.jseaes.2012.12.014

    CrossRef Google Scholar

    [29] L Ayalew, H Yamagishi. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan[J]. Geomorphology, 2005, 65(1): 15-31.

    Google Scholar

    [30] Binh Thai Pham, Biswajeet Pradhan, Dieu Tien Bui, et al. A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)[J]. Environmental Modelling & Software, 2016, 84: 240-250.

    Google Scholar

    [31] Wei Chen, Hamid Reza Pourghasemi, Mahdi Panahi, et al. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques[J]. Geomorphology, 2017, 297: 69-85. doi: 10.1016/j.geomorph.2017.09.007

    CrossRef Google Scholar

    [32] FC Dai, CF Lee, YY Ngai. Landslide risk assessment and management: an overview[J]. Engineering Geology, 2002, 64(1): 65-87. doi: 10.1016/S0013-7952(01)00093-X

    CrossRef Google Scholar

    [33] Francisco Gutiérrez, Rogelio Linares, Carles Roqué, et al. Large landslides associated with a diapiric fold in Canelles Reservoir (Spanish Pyrenees): Detailed geological geomorphological mapping, trenching and electrical resistivity imaging[J]. Geomorphology, 2015, 241: 224-242. doi: 10.1016/j.geomorph.2015.04.016

    CrossRef Google Scholar

    [34] Harlow L L. The Essence of multivariate thinking: Basic themes and methods[M]. London: Lawrence ErlbaumAssociates, 2005: 199-207.

    Google Scholar

    [35] Haydar Y. Hussin, Veronica Zumpano, Paola Reichenbach, et al. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model[J]. Geomorphology, 2016, 253: 508-523. doi: 10.1016/j.geomorph.2015.10.030

    CrossRef Google Scholar

    [36] Rumelhart d Hinton-G-Williams-R. . Learning representations by back-propagatingerrors[J]. Nature, 1986, 323: 533-536. doi: 10.1038/323533a0

    CrossRef Google Scholar

    [37] Faming Huang, Zhongshan Cao, Jianfei Guo, et al. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping[J]. Catena, 2020, 191: 104580. doi: 10.1016/j.catena.2020.104580

    CrossRef Google Scholar

    [38] Guan-Wei Lin, Hongey Chen, Tsun-Ying Shih, et al. Various links between landslide debris and sediment flux during earthquake and rainstorm events[J]. Journal of Asian Earth Sciences, 2012, 54-55: 41-48. doi: 10.1016/j.jseaes.2012.03.012

    CrossRef Google Scholar

    [39] Markus Meinhardt, Manfred Fink, Hannes Tuenschel. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics[J]. Geomorphology, 2015, 234: 80-97. doi: 10.1016/j.geomorph.2014.12.042

    CrossRef Google Scholar

    [40] Pham, BT, Shirzadi, et al. A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India[J]. Int J Sediment Res, 2018, 2018, 33(2): 157-170.

    Google Scholar

    [41] B. Pradhan. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches[J]. Journal of the Indian Society of Remote Sensing, 2010, 38(2): 301-320. doi: 10.1007/s12524-010-0020-z

    CrossRef Google Scholar

    [42] Ryuta Tamura, Ken Kobayashi, Yuichi Takano, et al. Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor[J]. Journal of Global Optimization, 2019, 73(2): 431-446. doi: 10.1007/s10898-018-0713-3

    CrossRef Google Scholar

    [43] Shen Chaoyong, Feng Zhongke, Xie Chou, et al. Refinement of Landslide Susceptibility Map Using Persistent Scatterer Interferometry in Areas of Intense Mining Activities in the Karst Region of Southwest China[J]. Remote Sensing, 2019, 11（23）: 2821-2843.

    Google Scholar

    [44] Vijendra Kumar Pandey, Kaushal Kumar Sharma, Hamid Reza Pourghasemi, et al. Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India[J]. Catena, 2019, 182: 104150. doi: 10.1016/j.catena.2019.104150

    CrossRef Google Scholar

    [45] Chao Zhou, Kunlong Yin, Ying Cao, et al. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China[J]. Engineering Geology, 2016, 204: 108-120. doi: 10.1016/j.enggeo.2016.02.009

    CrossRef Google Scholar

    [46] Zhifu Zhu, Shu Gan, Xiping Yuan, et al. Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)[J]. Sensors, 2022, 22(15): 5587. doi: 10.3390/s22155587

    CrossRef Google Scholar

    • Relative Articles
  • Cited by
  • Supplements
    • Proportional views
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(5)

Tables(3)

Export Citation
PDF
XML

Article Metrics

Article views(473) PDF downloads(5) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    版权所有：中国地质调查局发展研究中心 copyright @2020