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ZHANG Tianyu, LI Lincui, LIU Fan, HONG Zenglin, QIAN Faqiao, HU Bin, ZHANG Miao. 2025. Evaluation of Loess Landslide Susceptibility Based on Optimised MaxEnt Model: A Case Study of Wuqi County in Shaanxi Province. Northwestern Geology, 58(2): 172-185. doi: 10.12401/j.nwg.2024104
Citation: ZHANG Tianyu, LI Lincui, LIU Fan, HONG Zenglin, QIAN Faqiao, HU Bin, ZHANG Miao. 2025. Evaluation of Loess Landslide Susceptibility Based on Optimised MaxEnt Model: A Case Study of Wuqi County in Shaanxi Province. Northwestern Geology, 58(2): 172-185. doi: 10.12401/j.nwg.2024104
shu

Evaluation of Loess Landslide Susceptibility Based on Optimised MaxEnt Model: A Case Study of Wuqi County in Shaanxi Province

  • 1.

    Shaanxi Geological Environment Monitoring Station, Xi’an 710054, Shaanxi, China

  • 2.

    College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, Shaanxi, China

  • 3.

    Key Laboratory of Mine Geological Hazards Mechanism and Control, Xi’an 710054, Shaanxi, China

  • 4.

    61363 Troops, Xi’an 710054, Shaanxi, China

More Information
Received Date:  26 February 2024
Revised Date:  23 October 2024
Accepted Date:  22 November 2024
Publish Date:  20 April 2025
    Abstract

  • Landslide disasters which occur frequently in the Loess Plateau, seriously endanger the safety of people's lives and property, and affect the construction of major projects. Accurate landslide susceptibility assessment is useful for efficiently and quickly landslide risk prediction, and can provide scientific backing for disaster prevention and reduction by identifying "where landslides are prone". Taking Wuqi County on the Loess Plateau as an example, we use the optimized MaxEnt model and 505 landslide points to evaluate the landslide susceptibility. Elevation, aspect, slope, terrain roughness, lithology, river buffer, rainfall, NDWI (surface humidity), road buffer, and InSAR surface deformation data, which was introduced as dynamic evaluation factors, were selected as influencing factors. The results show: In the MaxEnt model based on Enmeval packet adjustment, when 90% landslide points were randomly selected as the training set and 10% landslide points as the verification set, the model accuracy was the highest (AUC value was 0.855), and the simulation effect was accurate and reliable. InSAR surface deformation rate was introduced as a dynamic evaluation factor, and the model accuracy and evaluation results were both improved. In the study area, the area of high and relatively high susceptibility areas accounted for 10.27% and 6.33% of the total area respectively, and the landslide points in the high and relatively high prone areas accounted for 73.27% of the total landslide points, of which the high prone areas accounted for 48.11%. The evaluation results of landslide susceptibility were consistent with the distribution of landslide points, which proves that the evaluation works well. Elevation, slope and surface roughness contribute significantly to the simulation results, and are important factors affecting the landslide susceptibility.

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  • [1] 陈舞, 王浩, 张国华, 等. 基于T-S模糊故障树和贝叶斯网络的隧道坍塌易发性评价[J]. 上海交通大学学报, 2020, 54（8）: 820−830.

    Google Scholar

    CHEN Wu, WANG Hao, ZHANG Guohua, et al. Evaluation of Tunnel Collapse Susceptibility Based on T-S Fuzzy Fault Tree and Bayesian Network[J]. Journal of Shanghai Jiaotong University,2020,54(8):820−830.

    Google Scholar

    [2] 段钊, 赵法锁, 陈新建. 陕北黄土高原区滑坡发育类型与时空分布特征——以吴起县为例[J]. 灾害学, 2011, 26（4）: 52−56.

    Google Scholar

    DUAN Zhao, ZHAO Fasuo, CHEN Xinjian. Types and Spatio-Temporal Distribution of Loess Landslides in Loess Plateau Region —A Case Study in Wuqi County[J]. Journal of Catastrophology,2011,26(4):52−56.

    Google Scholar

    [3] 郭飞, 赖鹏, 黄发明, 等. 基于知识图谱的滑坡易发性评价文献综述及研究进展[J]. 地球科学, 2024, 49（5）: 1587−1606.

    Google Scholar

    GUO Fei,LAI Peng,HUANG Faming,et al. Literature review and research progress of landslide susceptibility mapping based on knowledge graph[J]. Earth Science,2024,49(5):1587−1606.

    Google Scholar

    [4] 高秉海, 何毅, 张立峰, 等. 顾及InSAR形变的CNN滑坡易发性动态评估——以刘家峡水库区域为例[J]. 岩石力学与工程学报, 2023, 42（2）: 450−465.

    Google Scholar

    GAO Binghai, HE Yi, ZHANG Lifeng, et al. Dynamic evaluation of landslide susceptibility by CNN considering InSAR deformation: A case study of Liujiaxia reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(2):450−465.

    Google Scholar

    [5] 胡胜, 邱海军, 王宁练, 等. 地形对黄土高原滑坡的影响[J]. 地理学报, 2021, 76（11）: 2697−2709.

    Google Scholar

    HU Sheng, QIU Haijun, WANG Ninglian, et al. The influence of terrain on loess landslides in Loess Plateau[J]. Acta Geographica Sinica,2021,76(11):2697−2709.

    Google Scholar

    [6] 贾丽娜, 陈世昌. 基于AHP和GIS的舟曲地质灾害易发性评价[J]. 西北地质, 2024, 57（1）: 23−33.

    Google Scholar

    JIA Lina,CHEN Shichang. Geological Hazard Susceptibility Evaluation Based on AHP and GIS in Zhouqu County, Gansu[J]. Northwestern Geology,2024,57(1):23−33.

    Google Scholar

    [7] 胡燕, 李德营, 孟颂颂, 等. 基于证据权法的巴东县城滑坡灾害易发性评价[J]. 地质科技通报, 2020, 39（3）: 187−194.

    Google Scholar

    HU Yan, LI Deying, MENG Songsong, et al. Landslide susceptibility evaluation in Badong County based on weights of evidence method[J]. Bulletin of Geological Science and Technology,2020,39(3):187−194.

    Google Scholar

    [8] 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

    [9] 姜建伟, 赵法锁, 祝俊华, 等. 陕西省吴起县斜坡几何形态与地质灾害相关性研究[J]. 灾害学, 2018, 33（2）: 188−192.

    Google Scholar

    JIANG Jianwei, ZHAO Fasuo, ZHU Junhua, et al. Study on the correlation between slope geometrical morphology and geological disasters in Wuqi County of Shaanxi Province[J]. Journal of Catastrophology,2018,33(2):188−192.

    Google Scholar

    [10] 李泽芝, 王新刚. 镇域尺度下秦巴山区堆积层滑坡易发性不同单元评价性能对比研究[J]. 西北地质, 2024, 57（1）: 1−11.

    Google Scholar

    LI Zezhi,WANG Xingang. Comparative Study on Evaluation Performance of Different Units of Susceptibility of Accumulation Layer Landslide in Qinba Mountain Area at Town Scale[J]. Northwestern Geology,2024,57(1):1−11.

    Google Scholar

    [11] 林琴, 郭永刚, 吴升杰, 等. 基于梯度提升的优化集成机器学习算法对滑坡易发性评价: 以雅鲁藏布江与尼洋河两岸为例[J]. 西北地质, 2024, 57（1）: 12−22.

    Google Scholar

    LIN Qin,GUO Yonggang,WU Shengjie,et al. Evaluation of Landslide Susceptibility by Optimization Integrated Machine Learning Algorithm Based on Gradient Boosting: Take Both Banks of Yarlung Zangbo River and Niyang River as Examples[J]. Northwestern Geology,2024,57(1):12−22.

    Google Scholar

    [12] 刘东生, 安芷生, 文启忠, 等. 中国黄土的地质环境[J]. 科学通报, 1978（1）: 19−26.

    Google Scholar

    LIU Tongsheng, AN Zhisheng, WEN Qizhong, et al. Geological Environment of Chinese Loess[J]. Chinese Science Bulletin,1978(1):19−26.

    Google Scholar

    [13] 兰恒星, 彭建兵, 祝艳波, 等. 黄河流域地质地表过程与重大灾害效应研究与展望[J]. 中国科学: 地球科学, 2022, 65（2）: 234−256.

    Google Scholar

    LAN Hengxing,PENG Jianbing,ZHU Yanbo,et al. Research on geological and surfacial processes and major disaster effects in the Yellow River Basin[J]. Science China Earth Sciences,2022,65(2):234−256.

    Google Scholar

    [14] 麦鉴锋, 冼宇阳, 刘桂林. 气候变化情景下广东省降雨诱发型滑坡灾害潜在分布及预测[J]. 地球信息科学学报, 2021, 23（11）: 2042−2054. doi: 10.12082/dqxxkx.2021.210182

    CrossRef Google Scholar

    MAI Jianfeng, XIAN Yuyang, LIU Guilin. Predicting Potential Rainfall-Triggered Landslides Sites in Guangdong Province (China) using MaxEnt Model under Climate Changes Scenarios[J]. Journal of Geo-information Science,2021,23(11):2042−2054. doi: 10.12082/dqxxkx.2021.210182

    CrossRef Google Scholar

    [15] 牛全福, 冯尊斌, 党星海, 等. 黄土区滑坡研究中地形因子的选取与适宜性分析[J]. 地球信息科学学报, 2017, 19（12）: 1584−1592.

    Google Scholar

    NIU Quanfu, FENG Zunbin, DANG Xinghai, et al. Suitability analysis of topographic factors in Loess landslide research[J]. Journal of Geo-information Science,2017,19(12):1584−1592.

    Google Scholar

    [16] 彭建兵, 兰恒星, 钱会, 等. 宜居黄河科学构想[J]. 工程地质学报, 2020, 28（2）: 189−201.

    Google Scholar

    PENG Jianbing, LAN Hengxing, QIAN Hui, et al. Scientific research framework of livable Yellow River[J]. Journal of Engineering Geology,2020,28(2):189−201.

    Google Scholar

    [17] 彭建兵, 王启耀, 门玉明, 等. 黄土高原滑坡灾害[M]. 北京: 科学出版社, 2019.

    Google Scholar

    PENG Jianbing, WANG Qiyao, MEN Yuming, et al. Landslides in loess Plateau[M]. Beijing: Science Press, 2019.

    Google Scholar

    [18] 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

    [19] 孙长明, 马润勇, 尚合欣等. 基于滑坡分类的西宁市滑坡易发性评价[J]. 水文地质工程地质, 2020, 47（3）: 173−181.

    Google Scholar

    SUN Changming, MA Runyong, SHANG Hexin, et al. Landslide susceptibility assessment in Xining based on landslide classification[J]. Hydrogeology & Engineering Geology,2020,47(3):173−181.

    Google Scholar

    [20] 孙萍萍, 张茂省, 程秀娟, 等. 黄土高原地质灾害发生规律[J]. 山地学报, 2019, 37（5）: 737−746.

    Google Scholar

    SUN Pingping, ZHANG Maosheng, CHENG Xiujuan, et al. On the Regularity of Geological Hazards on the Loess Plateau in China[J]. Mountain Research,2019,37(5):737−746.

    Google Scholar

    [21] 唐亚明, 张茂省, 李林, 等. 滑坡易发性危险性风险评价例析[J]. 水文地质工程地质, 2011, 38（2）: 125−129.

    Google Scholar

    TANG Yaming, ZHANG Maosheng, LI Lin, et al. Discrimination to the landslide susceptibility, hazard and risk assessment[J]. Hydrogeology & Engineering Geology,2011,38(2):125−129.

    Google Scholar

    [22] 唐兴港, 王慧勇, 黄豆, 等. 长江中下游地区滑坡的易发性评价——以江西省为例[J]. 水土保持通报, 2021, 41（3）: 166−172.

    Google Scholar

    TANG Xinggang, WANG Huiyong, HUANG Dou, et al. Evaluation of Landslide Susceptibility in Middle and Lower Reaches of Yangtze River-Taking Jiangxi Province as a Caes Study[J]. Bulletin of Soiland Water Conservation,2021,41(3):166−172.

    Google Scholar

    [23] 唐亚明, 程秀娟, 薛强, 等. 基于层次分析法的黄土滑塌风险评价指标权重分析[J]. 中国地质灾害与防治学报, 2012, 23（4）: 40−46.

    Google Scholar

    TANG Yaming, CHENG Xiujuan, XUE Qiang, et al. Weights analysis of loess collapse risk assessing factors based on analytical hierarchy process[J]. The Chinese Journal of Geological Hazard and Control,2012,23(4):40−46.

    Google Scholar

    [24] 吴树仁, 石菊松, 张春山, 等. 地质灾害风险评估技术指南初论[J]. 地质通报, 2009, 28（8）: 995−1005.

    Google Scholar

    WU Shuren, SHI Jusong, ZHANG Chunshan, et al. Preliminary discussion on technical guideline for geohazard risk assessment[J]. Geological Bulletin of China,2009,28(8):995−1005.

    Google Scholar

    [25] 王本栋, 李四全, 许万忠, 等. 基于3种不同机器学习算法的滑坡易发性评价对比研究[J]. 西北地质, 2024, 57（1）: 34−43.

    Google Scholar

    WANG Bendong,LI Siquan,XU Wanzhong,et al. A Comparative Study of Landslide Susceptibility Evaluation Based on Three Different Machine Learning Algorithms[J]. Northwestern Geology,2024,57(1):34−43.

    Google Scholar

    [26] 王高峰, 郭宁, 邓兵, 等. 不同组合模型区域滑坡易发性及精度分析[J]. 西北地质, 2021, 54（2）: 259−272.

    Google Scholar

    WANG Gaofeng, GUO Ning, DENG Bing, et al. Analysis of Landslide Susceptibility and Accuracy in Different Combination Models[J]. Northwestern Geology,2021,54(2):259−272.

    Google Scholar

    [27] 王晓帆, 段雨营, 金露露, 等. 基于优化的最大嫡模型预测中国高山栋组植物的历史、现状与未来分布变化[J]. 生态学报, 2023, 43（16）: 6590−6604.

    Google Scholar

    WANG Xiaofan, DUAN Yuying, JIN Lulu, et al. Prediction of historical, present and future distribution of Ouercus sect. Heterobalanus based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica,2023,43(16):6590−6604.

    Google Scholar

    [28] 万洋, 郭捷, 马凤山, 等. 基于最大熵模型的中尼交通廊道滑坡易发性分析[J]. 中国地质灾害与防治学报, 2022, 33（2）: 88−95.

    Google Scholar

    WAN Yang, GUO Jie, MA Fengshan, et al. Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor[J]. The Chinese Journal of Geological Hazard and Control,2022,33(2):88−95.

    Google Scholar

    [29] 熊小辉, 汪长林, 白永健, 等. 基于不同耦合模型的县域滑坡易发性评价对比分析——以四川普格县为例[J]. 中国地质灾害与防治学报, 2022, 33（4）: 114−124.

    Google Scholar

    XIONG Xiaohui, WANG Changlin, BAI Yongjian, et al. Comparison of landslide susceptibility assessment based on multiple hybrid models at county level: A case study for Puge County, Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2022,33(4):114−124.

    Google Scholar

    [30] 于开宁, 吴涛, 魏爱华, 等. 基于AHP-突变理论组合模型的地质灾害危险性评价——以河北平山县为例[J]. 中国地质灾害与防治学报, 2023, 34（2）: 146−155.

    Google Scholar

    YU Kaining, WU Tao, WEI Aihua, et al. Geological hazard assessment based on the models of AHP, catastrophe theory and their combination: A case study in Pingshan County of Heibei Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(2):146−155.

    Google Scholar

    [31] 赵冬梅, 角媛梅, 邱应美, 等. 基于maxEnt模型的哈尼梯田核心区滑坡易发性评价[J]. 水土保持学报, 2020, 27（4）: 392−399.

    Google Scholar

    ZHAO Dongmei, JIAO Yuanmei, QIU Yingmei, et al. Assessment on Landslide Susceptibility of the Core Area of Hani Race Terraces Heritage Site Maximum Entropy Model[J]. Research of Soil and Water Conservation,2020,27(4):392−399.

    Google Scholar

    [32] 张伟, 周松林, 尹仑. 基于优化 MaxEnt 模型的高山峡谷区地质灾害易发性评价—以云南省德钦县为例[J]. 灾害学, 2023, 38（2）: 185−190.

    Google Scholar

    ZHANG Wei,ZHOU Songlin,YIN Lun. Evaluation of Susceptibility to Geological Hazard in Alpine Canyon Area Based on Optimized MaxEnt Model: A Case Study in Deqin County,Yunnan Province[J]. Journal of Catastrophology,2023,38(2):185−190.

    Google Scholar

    [33] 曾韬睿, 邬礼扬, 金必晶, 等. 基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图[J]. 岩石力学与工程学报, 2023, 42（9）: 2266−2282.

    Google Scholar

    ZENG Taorui, WU Liyang, JIN Bijing, et al. Landslide dynamic susceptibility mapping based on stacking ensemble strategy and SBAS-InSAR[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(9):2266−2282.

    Google Scholar

    [34] 朱耿平, 乔慧捷. Maxent模型复杂度对物种潜在分布区预测的影响[J]. 生物多样性, 2016, 24（10）: 1189−1196. doi: 10.17520/biods.2016265

    CrossRef Google Scholar

    ZHU Gengping, QIAO Huijie. Effect of the Maxent model’s complexity on the prediction of species potential distributions[J]. Biodiversity Science,2016,24(10):1189−1196. doi: 10.17520/biods.2016265

    CrossRef Google Scholar

    [35] 张茂省, 李同录. 黄土滑坡诱发因素及其形成机理研究[J]. 工程地质学报, 2011, 19（4）: 530−540.

    Google Scholar

    ZHANG Maosheng, LI Tonglu. Triggering factors and forming mechanism of loess landslide[J]. Journal of Engineering Geology,2011,19(4):530−540.

    Google Scholar

    [36] Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan[J]. Geomorphology,2005,65(1/2):15−31.

    Google Scholar

    [37] Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. Geoscience and Remote Sensing,2002,40(11):2375−2383. doi: 10.1109/TGRS.2002.803792

    CrossRef Google Scholar

    [38] Corominas J, Van W C J, Frattini P, et al. Recommendations for the Quantitative Analysis of Landslide Risk[J]. Bulletin of Engineering Geology and the Environment,2014,73(2):209−263.

    Google Scholar

    [39] Calderon-Guevara W, Sanchez-Silva M, Nitescu B, et al. Comparative Review of Data-driven Landslide Susceptibility Models: Case Study in the Eastern Andes Mountain Range of Colombia[J]. Natural Hazards,2022,113:1105−1132. doi: 10.1007/s11069-022-05339-2

    CrossRef Google Scholar

    [40] Calvello M, Peduto D, Arena L. Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides[J]. Landslides,2017,14(2):473−489. doi: 10.1007/s10346-016-0722-6

    CrossRef Google Scholar

    [41] Chao Zhou, Ying Cao, Xie Hu, et al. Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area[J]. Landslides,2022,19:1585−1597. doi: 10.1007/s10346-021-01796-1

    CrossRef Google Scholar

    [42] Fell R, Corominas J, Bonnard C, et al. Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land use Planning[J]. Engineering Geology,2008,102(3-4):85−98. doi: 10.1016/j.enggeo.2008.03.022

    CrossRef Google Scholar

    [43] Felicisimo Á M, Cuartero A, Remondo J, et al. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study[J]. Landslides,2013,10(2):175−189. doi: 10.1007/s10346-012-0320-1

    CrossRef Google Scholar

    [44] Huang Faming, Yao Chi, Liu Weiping, et al. Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine[J]. Geomatics,2018,1(9):919−938.

    Google Scholar

    [45] Hitoshi S, Daichi N, Hiroshi M. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan[J]. Geomorphology,2009,109(3-4):108−121. doi: 10.1016/j.geomorph.2009.02.026

    CrossRef Google Scholar

    [46] Kornejady A, Ownegh M, Bahrem A. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods[J]. Catena,2017,152:144−162. doi: 10.1016/j.catena.2017.01.010

    CrossRef Google Scholar

    [47] Lima P, Steger S, Glade T, et al. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility[J]. Journal of Mountain Science, 2022, 19(6): 1670-1698.

    Google Scholar

    [48] Lee S, Ryu J H, Won J S, et al. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network[J]. Engineering Geology,2004,71(3−4):289−302. doi: 10.1016/S0013-7952(03)00142-X

    CrossRef Google Scholar

    [49] Muscarella R, Galante P J, Soley-Guardia M, et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models[J]. Methods in Ecology & Evolution,2015,5(11):1198−1205.

    Google Scholar

    [50] Piacentini D, Devoto S, Mantovani M, et al. Landslide susceptibility modeling assisted by Persistent Scatterers Interferometry(PSI): an example from the northwestern coast of Malta[J]. Natural Hazards,2015,78(1):681−697. doi: 10.1007/s11069-015-1740-8

    CrossRef Google Scholar

    [51] Phillips S J, Erson R P, Dudik M, et al. Opening the black box: an open-source release of Maxent[J]. Ecography,2017,40(7):887−893. doi: 10.1111/ecog.03049

    CrossRef Google Scholar

    [52] Steven J P, Robert P A, Robert E S. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling,2006,190(3):231−259.

    Google Scholar

    [53] Tang Yaming, Feng Fan, Guo Zizheng, et al. Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the Loess Plateau area in Shanxi (China)[J]. Journal of Cleaner Production,2020,277(20):124−159.

    Google Scholar

    [54] Warren D L, Seifert S N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria[J]. Ecological Applications,2011,21(2):335−342. doi: 10.1890/10-1171.1

    CrossRef Google Scholar

    [55] Xu Ling, Yan Dongdong, Zhao Tengyuan. Probabilistic evaluation of loess landslide impact using multivariate model[J]. Landslides,2021,18(3):1011−1023. doi: 10.1007/s10346-020-01521-4

    CrossRef Google Scholar

    [56] Yuan Meijiao, Dong Meizhao, Yin Pingding, et al. Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China-ScienceDirect[J]. Catena,2019,183:104−221.

    Google Scholar

    [57] Yong Weili, Xian Minwang, Hang Mao. Influence of Human Activity on Landslide Susceptibility Development in the Three Gorges area[J]. Natural Hazards,2020,104:2115−2151. doi: 10.1007/s11069-020-04264-6

    CrossRef Google Scholar

    [58] Yikunoamlak G, Selemawi A, Esayas M, et al. Current and future predicting potential areas of Oxytenanthera abyssinica(A. Richard) using MaxEnt model under climate change in Northern Ethiopia[J]. Ecological Processes,2020,9(1):1−15. doi: 10.1186/s13717-019-0204-6

    CrossRef Google Scholar

    [59] Zhuang Jianqi, Peng Jianbing, Wang Gonghui, et al. Distribution and characteristics of landslide in Loess Plateau: a case study in Shaanxi Province[J]. Engineering Geology,2018,236(26):89−96.

    Google Scholar

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