Comparison Study in Landslide Susceptibility Assessment by Using Data-driven models: A Case Study from the Middle Stream of the Yellow River

LI Guangming; YANG Yufei; TANG Yaming; WANG Xiaohao; YIN Chunwang; FENG Fan; ZHOU Yongheng

doi:10.12401/j.nwg.2024064

2025 Vol. 58, No. 2

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Article navigation > Northwestern Geology > 2025 > 58(2): 51-65

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LI Guangming, YANG Yufei, TANG Yaming, WANG Xiaohao, YIN Chunwang, FENG Fan, ZHOU Yongheng. 2025. Comparison Study in Landslide Susceptibility Assessment by Using Data-driven models: A Case Study from the Middle Stream of the Yellow River. Northwestern Geology, 58(2): 51-65. doi: 10.12401/j.nwg.2024064

Citation:

LI Guangming, YANG Yufei, TANG Yaming, WANG Xiaohao, YIN Chunwang, FENG Fan, ZHOU Yongheng. 2025. Comparison Study in Landslide Susceptibility Assessment by Using Data-driven models: A Case Study from the Middle Stream of the Yellow River. Northwestern Geology, 58(2): 51-65. doi: 10.12401/j.nwg.2024064

Comparison Study in Landslide Susceptibility Assessment by Using Data-driven models: A Case Study from the Middle Stream of the Yellow River

1.
Tianjin Municipal Engineering Design & Research Institute, Tianjin 300392, China
2.
Zhejiang geology and mineral technology co. LTD, Hangzhou 310007, Zhejiang, China
3.
School of Civil and Transportation Engineering, Hebei University of Technology,Tianjin 300401, China
4.
Xi’an Center of China Geological Survey, Xi’an 710119, Shaanxi, China
5.
Shaanxi Nuclear Industry Engineering Survey Institute Co., Ltd. Xi’an 710054, Shaanxi, China

More Information

Corresponding author: TANG Yaming, tangyaming@mail.cgs.gov.cn

Received Date: 28 September 2022

Revised Date: 27 May 2024

Publish Date: 20 April 2025

Abstract

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.
- landslide susceptibility /
- weight /
- data-driven model /
- decision tree /
- environmental factors /
- random forest

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References

[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. Google Scholar 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. Google Scholar
[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. Google Scholar 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. Google Scholar
[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. Google Scholar 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. Google Scholar
[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. Google Scholar 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. Google Scholar
[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. Google Scholar 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. Google Scholar
[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. Google Scholar 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. Google Scholar
[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. 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
[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. 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
[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. Google Scholar 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. 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]	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. Google Scholar 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. Google Scholar
[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 CrossRef Google Scholar 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 CrossRef Google Scholar
[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. 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
[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. Google Scholar 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. Google Scholar
[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 CrossRef Google Scholar 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 CrossRef Google Scholar
[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. Google Scholar 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. Google Scholar
[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. Google Scholar 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. Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar
[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. Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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 CrossRef Google Scholar
[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. Google Scholar