Comparison Study in Landslide Susceptibility Assessment by Using Data-driven models: A Case Study from the Middle Stream of the Yellow River
-
摘要:
准确的滑坡易发性图有益于管理部门开展土地利用规划和防灾减灾工作，目前已经成为了中国滑坡风险评估与管控的重点研究领域。本研究旨在对比分析不同数据驱动模型在区域滑坡易发性评估中的表现，以黄河中游流域为研究区，通过详细的野外调查结合遥感图像视觉解释，获得了包括684个历史滑坡点的数据库。选取了14个评价因子，利用Pearson相关系数分析了这些因素之间的相关性，应用C5.0决策树算法确定了各因素的重要性。选取了3种典型的数据驱动模型（加权信息量（WIV），支持向量机（SVM）和随机森林（RF））进行了区域滑坡易发性评价，并通过受试者工作特征曲线（ROC）及其曲线下面积AUC值来验证模型的性能。结果表明，距道路的距离、距河流的距离以及坡度是该地区滑坡发生最重要的贡献因素。大多数历史滑坡都发生在滑坡易发性图中的中等和高易发区内。SVM和RF模型获得的高/极高易发区内的滑坡点均超过总滑坡点的70%。RF模型表现最好，高易发性区占全区面积的21.9%，滑坡数量占全部历史滑坡点的90.5%。AUC精度的比较表明，RF模型的准确性高于其他两种模型：RF的AUC为0.904，而WIV和SVM的AUC分别为0.845和0.847。
Abstract:Accurate landslide susceptibility maps are beneficial for management departments to carry out land use planning and disaster prevention and mitigation. It has been an important field in the landslide risk assessment and management in China. This study aims to compare and analyze the performance of different data-driven models in the assessment of regional landslide susceptibility. The middle reaches of the Yellow river were selected as the study area, and a database including 684 historical landslide points was obtained through detailed field investigation combined with visual interpretation of remote sensing images. 14 evaluation factors were selected, Pearson correlation coefficient was used to analyze the correlation between these factors, and the C5.0 decision tree algorithm was used to determine the importance of each factor. Three typical data-driven models (Weighted Information Volume (WIV), Support Vector Machine (SVM) and Random Forest (RF)) were selected to evaluate the regional landslide susceptibility, and the performance of the models were verified by the Receiver Operating Characteristic (ROC) curve and the area AUC value under the curve. The results show that the distance from the road, the distance from the river and the slope are the most important contributing factors to the occurrence of landslides in this area. The majority of historical landslides occurred in the moderate and high susceptibility zones on the landslide susceptibility map. The landslide points in the high/very high susceptibility area obtained by SVM and RF models exceed 70% of the total landslide points. The RF model performed the best, with the high susceptibility area accounting for 21.9% of the area and the number of landslides accounting for 90.5% of all historical landslide points. A comparison of AUC accuracy shows that the RF model is more accurate than the other two models: RF has an AUC of 0.904, while WIV and SVM have AUCs of 0.845 and 0.847 respectively.
-
Key words:
- landslide susceptibility /
- weight /
- data-driven model /
- decision tree /
- environmental factors /
- random forest
-
-
表 1 滑坡环境因子 IV 的计算结果
Table 1. Calculation of the IV of the landslide environmental factors
环境因子 值 Ni/N Si/S 密度比 信息量 权重 加权信息量 排名 高程 （m） 594～774 0.39 0.39 1.00 0.90 0.073 0.0657 9 774～901 0.22 0.76 0.29 −0.33 0.02409 61 901～ 1028 0.26 0.69 0.38 −0.07 0.00511 40 1028 ～1183 0.11 0.51 0.21 −0.67 0.04891 66 1183 ～1510 0.01 0.08 0.16 −0.92 0.06716 68 坡度 （°） 0～7 0.14 0.19 0.74 −0.30 0.107 − 0.0321 62 7～12 0.20 0.29 0.68 −0.39 0.04173 64 12～17 0.26 0.28 0.96 −0.05 0.00535 42 17～23 0.25 0.18 1.39 0.33 0.03531 15 23～59 0.14 0.06 2.44 0.89 0.09523 4 坡向 （°） 北（0～22.5） 0.09 0.06 1.56 0.45 0.031 0.01395 22 东北（22.5～67.5） 0.13 0.12 1.13 0.12 0.00372 28 东（67.5～112.5） 0.15 0.13 1.14 0.13 0.00403 27 东南（112.5～157.5） 0.10 0.12 0.79 −0.23 0.00713 44 南（157.5～202.5） 0.12 0.13 0.94 −0.06 0.00186 34 西南（202.5～247.5） 0.10 0.15 0.68 −0.38 0.01178 51 西（247.5～292.5） 0.09 0.14 0.68 −0.39 0.01209 52 西北（292.5～337.5） 0.14 0.11 1.27 0.24 0.00744 25 北（337.5-360） 0.08 0.05 1.57 0.45 0.01395 22 平面曲率 −2.824～−0.345 0.06 0.06 1.00 −0.16 0.031 0.00496 38 −0.345～−0.097 0.22 0.25 0.88 −0.12 0.00372 36 −0.097～0.095 0.35 0.34 1.03 0.03 0.00093 31 0.095～0.343 0.31 0.27 1.13 0.12 0.00372 28 0.343～4.227 0.07 0.07 1.00 −0.10 − 0.0031 35 剖面曲率 −3.908～−0.393 0.05 0.06 0.85 −0.16 0.031 0.00496 38 −0.393～−0.140 0.16 0.20 0.79 −0.23 0.00713 44 −0.140～0.082 0.28 0.37 0.77 −0.26 0.00806 47 0.082～0.367 0.36 0.29 1.23 0.20 0.0062 26 0.367～4.199 0.15 0.08 1.84 0.61 0.01891 18 地表粗糙度 1～1.023 0.34 0.48 0.69 −0.37 0.042 0.01554 55 1.023～1.052 0.33 0.32 1.02 0.02 0.00084 32 1.052～1.097 0.21 0.15 1.41 0.35 0.0147 21 1.097～1.199 0.09 0.04 2.23 0.80 0.0336 16 1.199～1.919 0.03 0.01 3.00 2.15 0.0903 8 岩性 沙壤土 0.16 0.19 0.88 −0.13 0.052 0.00676 43 黏土 0.33 0.43 0.77 −0.26 0.01352 53 红黏土 0.06 0.03 2.00 0.50 0.026 17 砂岩 0.43 0.31 1.38 0.32 0.01664 19 石灰岩 0.02 0.04 0.49 −0.71 0.03692 63 距断层距离 （m） 0～ 2709.069 0.37 0.28 1.30 0.26 0.049 0.01274 24 2709.069 ～5727.746 0.29 0.28 1.03 0.03 0.00147 30 5727.746 ～9056.030 0.16 0.19 0.83 −0.18 0.00882 48 9056.030 ～13003.531 0.11 0.16 0.72 −0.32 0.01568 56 13003.531 ～19814.904 0.07 0.09 0.79 −0.23 0.01127 50 NDWI −0.475～−0.235 0.06 0.10 0.56 −0.58 0.038 0.02204 60 −0.235～−0.196 0.25 0.37 0.69 −0.38 0.01444 54 −0.196～−0.151 0.47 0.43 1.09 −0.02 0.00076 33 −0.151～0.008 0.13 0.06 2.19 1.07 0.04066 11 0.008～0.240 0.09 0.04 2.27 2.47 0.09386 5 NDVI −0.198～0.008 0.02 0.01 2.00 1.13 0.038 0.04294 10 0.008～0.135 0.31 0.20 1.53 0.43 0.01634 20 0.135～0.180 0.35 0.40 0.88 −0.13 0.00494 37 0.180～0.235 0.26 0.30 0.87 −0.14 0.00532 41 0.235～0.536 0.05 0.09 0.57 −0.57 0.02166 59 距河流距离 （m） 0～100 0.35 0.15 2.33 0.85 0.136 0.1156 3 100～200 0.16 0.12 1.32 0.28 0.03808 14 200～300 0.11 0.13 0.86 −0.15 − 0.0204 58 300～500 0.14 0.21 0.66 −0.41 0.05576 67 500～ 1776.851 0.23 0.38 0.60 −0.51 0.06936 69 距道路距离 （m） 0～100 0.72 0.16 4.56 1.52 0.159 0.24168 1 100～200 0.12 0.12 0.95 −0.05 0.00795 46 200～300 0.05 0.12 0.41 −0.90 − 0.1431 71 300～500 0.06 0.18 0.34 −1.07 0.17013 72 500～ 2835.437 0.05 0.42 0.12 −2.11 0.33549 74 土地利用 水面 0.03 0.02 1.65 0.50 0.08 0.04 12 村庄 0.17 0.05 3.22 1.17 0.0936 6 林地 0.24 0.28 0.87 −0.14 − 0.0112 49 草地 0.28 0.48 0.58 −0.54 − 0.0432 65 农田 0.29 0.17 1.66 0.50 0.04 12 降雨 （mm） ＜400 0.10 0.22 0.47 −1.45 0.132 − 0.1914 73 400～425 0.22 0.11 1.99 0.69 0.09108 7 425～450 0.12 0.13 0.87 −0.14 0.01848 57 450～475 0.10 0.22 0.44 −0.82 0.10824 70 ＞475 0.46 0.33 1.40 1.57 0.20724 2 
下载: 导出CSV
-
[1] 付泉, 党光普, 李致博, 等. 基于分形维数耦合支持向量机和熵权模型的滑坡易发性研究[J]. 西北地质, 2024, 57（6）: 255−267.
FU Quan,DANG Guangpu,LI Zhibo,et al. Study of Landslide Susceptibility Mapping Based on Fractal Dimension Integrating Support Vector Machine with Index of Entropy Model[J]. Northwestern Geology,2024,57(6):255−267.
[2] 郭子正, 殷坤龙, 黄发明, 等. 基于地表监测数据和非线性时间序列组合模型的滑坡位移预测[J]. 岩石力学与工程学报, 2018, 37（S1）: 3392−3399.
GUO Zizheng, YIN Kunlong, HUANG Faming, et al. Landslide displacement prediction based on surface monitoring data and nonlinear time series combination model[J]. Chinese Journal of Rock Mechanics and Engineering,2018,37(S1):3392−3399.
[3] 郭子正, 殷坤龙, 黄发明, 等. 基于滑坡分类和加权频率比模型的滑坡易发性评价[J]. 岩石力学与工程学报, 2019a, 38（2）: 287−300.
GUO Zizheng, YIN Kunlong, HUANG Faming, et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering,2019a,38(2):287−300.
[4] 郭子正, 殷坤龙, 付圣, 等. 基于GIS与WOE-BP模型的滑坡易发性评价[J]. 地球科学, 2019b, 44（12）: 4299−4312.
GUO Zizheng, YIN Kunlong, FU Sheng, et al. Evaluation of landslides susceptibility based on GIS and WOE-BP model[J]. Earth Science,2019b,44(12):4299−4312.
[5] 黄发明, 叶舟, 姚池, 等. 滑坡易发性预测不确定性: 环境因子不同属性区间划分和不同数据驱动模型的影响[J]. 地球科学, 2020, 45（12）: 4535−4549.
HUANG Faming,YE Zhou,YAO Chi,et al. Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models[J]. Earth Science,2020,45(12):4535−4549.
[6] 黄发明, 陈佳武, 唐志鹏, 等. 不同空间分辨率和训练测试集比例下的滑坡易发性预测不确定性[J]. 岩石力学与工程学报, 2021, 40（6）: 1155−1169.
HUANG Faming, CHEN Jiawu, TANG Zhipeng, et al. Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models[J]. Chinese Journal of Rock Mechanics and Engineering,2021,40(6):1155−1169.
[7] 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.
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.
[8] 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.
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.
[9] 李婧, 卢玲, 唐泽. 基于TRIGRS模型的区域降雨型浅层滑坡危险性评价[J]. 甘肃水利水电技术, 2022, 58: 24−27.
LI Jing, LU Ling, TANG Ze. Risk assessment of regional rainfall-type shallow landslide based on TRIGRS model[J]. Gansu Water Resources and Hydropower Technology,2022,58:24−27.
[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.
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.
[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.
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.
[12] 林明明, 赵勇, 王坤, 等. 基于多源时序InSAR技术的滑坡隐患早期识别[J]. 西北地质, 2024, 57（6）: 268−277.
LIN Mingming,ZHAO Yong,WANG Kun,et al. Early Identification of Potential Dangers of Loess Landslide Based on Multi-Source and Time Series InSAR[J]. Northwestern Geology,2024,57(6):268−277.
[13] 田乃满, 兰恒星, 伍宇明, 等. 人工神经网络和决策树模型在滑坡易发性分析中的性能对比[J]. 地球信息科学学报, 2020, 22（12）: 2304−2316. doi: 10.12082/dqxxkx.2020.190766
TIAN Naiman, LAN Hengxing, WU Yuming, et al. Performance Comparison of BP Artificial Neural Network and CART Decision Tree Model in Landslide Susceptibility Prediction[J]. Journal of Geo-Information Science,2020,22(12):2304−2316. doi: 10.12082/dqxxkx.2020.190766
[14] 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.
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.
[15] 武利. 基于SINMAP模型的区域滑坡危险性定量评估及模型验证[J]. 地理与地理信息科学, 2012, 28（2）: 35−39+113.
WU Li. Quantitative assessment and model validation of regional landslide risk based on SINMAP model[J]. Geography and Geographic Information Science,2012,28(2):35−39+113.
[16] 许冲, 戴福初, 姚鑫, 等. 基于GIS与确定性系数分析方法的汶川地震滑坡易发性评价[J]. 工程地质学报, 2010, 18（1）: 15−26. doi: 10.3969/j.issn.1004-9665.2010.01.003
XU Chong, DAI Fuchu, YAO Xin, et al. Landslide susceptibility evaluation of Wenchuan earthquake based on GIS and deterministic coefficient analysis method[J]. Chinese Journal of Engineering Geology,2010,18(1):15−26. doi: 10.3969/j.issn.1004-9665.2010.01.003
[17] 张俊, 殷坤龙, 王佳佳, 等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报, 2016, 35（2）: 284−296.
ZHANG Jun, YIN Kunlong, WAND Jiajia, et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2016,35(2):284−296.
[18] 张林梵. 基于时序InSAR的黄土滑坡隐患早期识别—以白鹿塬西南区为例[J]. 西北地质, 2023, 56（3）: 250−257.
ZHANG Linfan. Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan[J]. Northwestern Geology,2023,56(3):250−257.
[19] Bhandary N P, Dahal R K, Timilsina M, et al. Rainfall event-based landslide susceptibility zonation mapping[J]. Natural Hazards,2013,69(1):365−388. doi: 10.1007/s11069-013-0715-x
[20] Binh T P, Jaafari A, Prakash I, et al. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling[J]. Bulletin of Engineering Geology and the Environment,2019,78(4):2865−2886. doi: 10.1007/s10064-018-1281-y
[21] Bueechi E, Klimes J, Frey H, et al. Regional-scale landslide susceptibility modelling in the Cordillera Blanca, Perua comparison of different approaches[J]. Landslides,2019,16(2):395−407. doi: 10.1007/s10346-018-1090-1
[22] Catani F, Lagomarsino D, Segoni S, et al. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues[J]. Natural Hazards and Earth System Sciences,2013,13(11):2815−2831. doi: 10.5194/nhess-13-2815-2013
[23] Chang Z, Du Z, Zhang F, et al. Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models[J]. Remote Sensing,2020,12(3):502. doi: 10.3390/rs12030502
[24] Cherkassky V. The nature of statistical learning theory[J]. IEEE transactions on neural networks,1997,8(6):1564. doi: 10.1109/TNN.1997.641482
[25] Dou J, Yunus A P, Dieu T B, et al. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan[J]. Landslides,2020,17(3):641−658. doi: 10.1007/s10346-019-01286-5
[26] Fell R, Cororninas 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
[27] Froude M J, Petley D N. Global fatal landslide occurrence from 2004 to 2016[J]. Natural Hazards and Earth System Sciences,2018,18(8):2161−2181. doi: 10.5194/nhess-18-2161-2018
[28] Goetz J N, Brenning A, Petschko H, et al. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling[J]. Computers & Geosciences,2015,81:1−11.
[29] Goetz J N, Guthrie R H, Brenning A. Integrating physical and empirical landslide susceptibility models using generalized additive models[J]. Geomorphology,2011,129(3-4):376−386. doi: 10.1016/j.geomorph.2011.03.001
[30] Guo Z, Shi Y, Huang F, et al. Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management[J]. Geoscience Frontiers,2021,12(6):101249. doi: 10.1016/j.gsf.2021.101249
[31] Guo Z, Yin K, Liu Q, et al. Rainfall Warning of Creeping Landslide in Yunyang County of Three Gorges Reservoir Region Based on Displacement Ratio Model[J]. Earth Science,2020,45(2):672−684.
[32] Guo Z, Wang H, He J, et al. PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment[J]. Environmental Modelling & Software,2025,186:106367.
[33] Guzzetti F, Reichenbach P, Cardinali M, et al. Probabilistic landslide hazard assessment at the basin scale[J]. Geomorphology,2005,72(1-4):272−299. doi: 10.1016/j.geomorph.2005.06.002
[34] He J, Qiu H, Qu F, et al. Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models[J]. Catena,2021,197:104999. doi: 10.1016/j.catena.2020.104999
[35] Huang F, Yin K, Huang J, et al. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine[J]. Engineering Geology,2017,223:11−22. doi: 10.1016/j.enggeo.2017.04.013
[36] Huang Y, Xu C, Zhang X, et al. An Updated Database and Spatial Distribution of Landslides Triggered by the Milin, Tibet M(w)6.4 Earthquake of 18 November 2017[J]. Journal of Earth Science,2021,32(5):1069−1078. doi: 10.1007/s12583-021-1433-z
[37] Hungr O, Leroueil S, Picarelli L. The Varnes classification of landslide types, an update[J]. Landslides,2014,11(2):167−194. doi: 10.1007/s10346-013-0436-y
[38] Kouli M, Loupasakis C, Soupios P, et al. Landslide susceptibility mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island, Greece[J]. Environmental Earth Sciences,2014,72(12):5197−5219. doi: 10.1007/s12665-014-3389-0
[39] Nsengiyumva J B, Valentino R. Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment[J]. Geomatics Natural Hazards & Risk,2020,11(1):1250−1277.
[40] Pereira S, Zezere J L, Bateira C. Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models[J]. Natural Hazards and Earth System Sciences,2012,12(4):979−988. doi: 10.5194/nhess-12-979-2012
[41] Pradhan B. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS[J]. Computers & Geosciences,2013,51:350−365.
[42] Reichenbach P, Rossi M, Malamud B D, et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews,2018,180:60−91. doi: 10.1016/j.earscirev.2018.03.001
[43] Rossi M, Guzzetti F, Reichenbach P, et al. Optimal landslide susceptibility zonation based on multiple forecasts[J]. Geomorphology,2010,114(3):129−142. doi: 10.1016/j.geomorph.2009.06.020
[44] Segoni S, Pappafico G, Luti T, et al. Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization[J]. Landslides,2020,17(10):2443−2453. doi: 10.1007/s10346-019-01340-2
[45] Sezer E A, Nefeslioglu H A, Osna T. An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software[J]. Computers & Geosciences,2017,98:26−37.
[46] Tang Y, Feng F, Guo Z, 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,2020,277:124159. doi: 10.1016/j.jclepro.2020.124159
[47] Wang Q, Guo Y, Li W, et al. Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor[J]. Geomatics Natural Hazards & Risk,2019,10(1):820−835.
[48] Wang Y, Feng L, Li S, et al. A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China[J]. Catena,2020,188:104425. doi: 10.1016/j.catena.2019.104425
[49] Yao X, Tham L G, Dai F. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China[J]. Geomorphology,2008,101(4):572−582. doi: 10.1016/j.geomorph.2008.02.011
[50] Zêzere J L, Pereira S, Melo R, et al. Mapping landslide susceptibility using data-driven methods[J]. Science of The Total Environment,2017,589:250−267. doi: 10.1016/j.scitotenv.2017.02.188
[51] Zhang M, Liu J. Controlling factors of loess landslides in western China[J]. Environmental Earth Sciences,2010,59(8):1671−1680. doi: 10.1007/s12665-009-0149-7
[52] Zhou C, Yin K, Cao Y, 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
[53] Zhu L, Wang G, Huang F, et al. Landslide Susceptibility Prediction Using Sparse Feature Extraction and Machine Learning Models Based on GIS and Remote Sensing[J]. Ieee Geoscience and Remote Sensing Letters,2022,19:1−5.
-
图(9)
表(1)
计量
- 文章访问数: 149
- PDF下载数: 23
- 施引文献: 0