Quantitative assessment of landslide hazard susceptibility and key driving factors in loess plateau geomorphologic area

CHEN Danlu; AN Xuelian; SHAO Huaiyong; LI Wenran; PAN Mingchen; WEN Haijia; SUN Deliang

doi:10.12202/j.0476-0301.2024227

Journal of Beijing Normal University(Natural Science) > 2025 > 61(2): 255-267. > DOI: 10.12202/j.0476-0301.2024227

CHEN Danlu, AN Xuelian, SHAO Huaiyong, LI Wenran, PAN Mingchen, WEN Haijia, SUN Deliang. Quantitative assessment of landslide hazard susceptibility and key driving factors in loess plateau geomorphologic area[J]. Journal of Beijing Normal University(Natural Science), 2025, 61(2): 255-267. DOI: 10.12202/j.0476-0301.2024227

Citation:

PDF (6085 KB)

Quantitative assessment of landslide hazard susceptibility and key driving factors in loess plateau geomorphologic area

CHEN Danlu¹,
AN Xuelian^{2, 3, ,},
SHAO Huaiyong¹,
LI Wenran⁴,
PAN Mingchen^{2, 3},
WEN Haijia⁵,
SUN Deliang^{2, 3}

1.
College of Geography and Planning，Chengdu University of Technology，Chengdu，Sichuan，China
2.
Chongqing University Key Laboratory of GIS Application Research，Chongqing Normal University，Chongqing，China
3.
College of Geography and Tourism，Chongqing Normal University，Chongqing，China
4.
CO-OP Environmental Technology Co，Ltd，Beijing，China
5.
Key Laboratory of New Technology for Construction of Cities in Mountain Area，Chongqing University，Chongqing，China

More Information

Received Date: September 28, 2024
Accepted Date: September 28, 2024
Available Online: February 21, 2025

Graphical Abstract

Abstract

Abstract

Landslide has always been an important issue in engineering geology; most studies focus on landslide disasters in mountainous areas, few studies focus on landslide disasters in the loess plateau area． Loess plateau, an important geographic unit in northern China, has special geological structures, under certain climatic conditions often leads to landslides, mudslides and other disasters, causing great disasters and property losses． In this paper, landslide disaster in loess plateau geomorphology area is studied, to establish a comprehensive quantitative evaluation system for landslide susceptibility by comprehensively considering factors of topography and geomorphology, hydrology, soil and other factors． LightGBM is used to simulate landslide susceptibility of loess plateau． Five indicators are selected to evaluate prediction performance and robustness． SHAP algorithm is used to analyze influence of triggering factors on the simulation results, with mechanism of landslide susceptibility in loess plateau geomorphologic area revealed． LightGBM model could simulate and predict susceptibility of landslides in loess plateau rather well (AUC = 0.844)． SHAP algorithm identifies key driving factors: distance from road, annual average rainfall, degree of topographic undulation, density of population, depth of ground surface cutting, length of slope. Landslide susceptibility in the study area has been mapped． A risk map was drawn. This work provides solid theoretical basis for regional landslide management and landslide disaster prevention and mitigation．
- loess landslide susceptibility assessment,
- loess plateau landforms,
- machine learning,
- interpretability analysis

FullText(HTML)

References (51)

References

[1]	崔宗培. 中国水利百科全书:第2卷. [M]. 北京:中国水利水电出版社,2006
[2]	顾磊,黄河. 滑坡地质灾害防治研究进展[J]. 地球科学前沿,2021,11(6):786
[3]	PETLEY D. Global patterns of loss of life from landslides[J]. Geology,2012,40(10):927 doi: 10.1130/G33217.1
[4]	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 doi: 10.5194/nhess-18-2161-2018
[5]	LIU T S. Loess and the Environment[M]. Beijing:Science Press,1985
[6]	柴慧霞,程维明,乔玉良. 中国 “数字黄土地貌” 分类体系探讨[J]. 地球信息科学,2006,8(2):6
[7]	XU X Z,GUO W Z,LIU Y K,et al. Landslides on the Loess Plateau of China:a latest statistics together with a close look[J]. Natural Hazards,2017,86(3):1393 doi: 10.1007/s11069-016-2738-6
[8]	MENG Z J,MA P H,PENG J B. Characteristics of loess landslides triggered by different factors in the Chinese Loess Plateau[J]. Journal of Mountain Science,2021,18(12):3218 doi: 10.1007/s11629-021-6880-6
[9]	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
[10]	MENG X M,DERBYSHIRE E. Landslides and their control in the Chinese Loess Plateau:models and case studies from Gansu province,China[J]. Geological Society,London,Engineering Geology Special Publications,1998,15(1):141 doi: 10.1144/GSL.ENG.1998.015.01.15
[11]	王念秦. 黄土滑坡发育规律及其防治措施研究[D]. 成都:成都理工大学,2004
[12]	李阳,张建军,魏广阔,等. 晋西黄土区极端降雨后浅层滑坡调查及影响因素分析[J]. 水土保持学报,2022,36(5):44
[13]	DIKSHIT A,SARKAR R,PRADHAN B,et al. Spatial landslide risk assessment at Phuentsholing,Bhutan[J]. Geosciences,2020,10(4):131 doi: 10.3390/geosciences10040131
[14]	CHEN Y,DONG J L,GUO F,et al. Review of landslide susceptibility assessment based on knowledge mapping[J]. Stochastic Environmental Research and Risk Assessment,2022,36(9):2399 doi: 10.1007/s00477-021-02165-z
[15]	NEULAND H. A prediction model of landslips[J]. CATENA,1976,3(2):215 doi: 10.1016/0341-8162(76)90011-4
[16]	CARRARA A. Multivariate models for landslide hazard evaluation[J]. Journal of the International Association for Mathematical Geology,1983,15(3):403 doi: 10.1007/BF01031290
[17]	MEJIA-NAVARRO M,WOHL E E. Geological hazard and risk evaluation using GIS:methodology and model applied to Medellin,Colombia[J]. Environmental & Engineering Geoscience,1994,31(4):459
[18]	LEE S,MIN K. Statistical analysis of landslide susceptibility at Yongin,Korea[J]. Environmental Geology,2001,40(9):1095 doi: 10.1007/s002540100310
[19]	MERGHADI A,YUNUS A P,DOU J,et al. Machine learning methods for landslide susceptibility studies:a comparative overview of algorithm performance[J]. Earth-Science Reviews,2020,207:103225 doi: 10.1016/j.earscirev.2020.103225
[20]	KAVZOGLU T,COLKESEN I,SAHIN E K. Machine learning techniques in landslide susceptibility mapping:a survey and a case study[M]//PRADHAN S,VISHAL V,SINGH T. Landslides:Theory,Practice and Modelling. Cham:Springer,2019:283
[21]	MARJANOVIĆ M,KOVAČEVIĆ M,BAJAT B,et al. Landslide susceptibility assessment using SVM machine learning algorithm[J]. Engineering Geology,2011,123(3):225 doi: 10.1016/j.enggeo.2011.09.006
[22]	YOUSSEF K,SHAO K,MOON S,et al. Landslide susceptibility modeling by interpretable neural network[J]. Communications Earth & Environment,2023,4:162
[23]	BARREDO ARRIETA A,DÍAZ-RODRÍGUEZ N,DEL SER J,et al. Explainable Artificial Intelligence (XAI):concepts,taxonomies,opportunities and challenges toward responsible AI[J]. Information Fusion,2020,58:82 doi: 10.1016/j.inffus.2019.12.012
[24]	GUNNING D,AHA D W. DARPA’s explainable artificial intelligence program[J]. AI Magazine,2019,40(2):44 doi: 10.1609/aimag.v40i2.2850
[25]	FLEMING S W,WATSON J R,ELLENSON A,et al. Machine learning in Earth and environmental science requires education and research policy reforms[J]. Nature Geoscience,2021,14(12):878 doi: 10.1038/s41561-021-00865-3
[26]	SUN D L,GU Q Y,WEN H J,et al. A hybrid landslide warning model coupling susceptibility zoning and precipitation[J]. Forests,2022,13(6):827 doi: 10.3390/f13060827
[27]	ALNAHIT A O,MISHRA A K,KHAN A A. Stream water quality prediction using boosted regression tree and random forest models[J]. Stochastic Environmental Research and Risk Assessment,2022,36(9):2661 doi: 10.1007/s00477-021-02152-4
[28]	ZHOU J,LI E M,YANG S,et al. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories[J]. Safety Science,2019,118:505 doi: 10.1016/j.ssci.2019.05.046
[29]	GUO X J,LI Y,LING H B. LIME:low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing,2017,26(2):982 doi: 10.1109/TIP.2016.2639450
[30]	沈玉昌. 中国地貌的类型与区划问题的商榷[J]. 第四纪研究,1958,1(1):33
[31]	罗来兴. 划分晋西,陕北,陇东黄土区域沟间地与沟谷的地貌类型[J]. 地理学报,1956,22(3):201
[32]	李喜安,骆建文,刘振山,等. 黄土地貌分类体系研究[J]. 工程地质学报,2016,24(S1):283
[33]	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
[34]	MENG Q,KE G L,WANG T F,et al. A communication-efficient parallel algorithm for decision tree[C]// 30th Conference on Neural Information Processing Systems. Barcelona,Spain:ACM,2016:694.
[35]	ALSABTI K,RANKA S,SINGH V. CLOUDS:a decision tree classifier for large datasets[C]//Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining. New York:Syracuse University,1998:2
[36]	JIN R M,AGRAWAL G. Communication and memory efficient parallel decision tree construction[C]//Proceedings of the 2003 SIAM International Conference on Data Mining. Philadelphia,PA:Society for Industrial and Applied Mathematics,2003:119
[37]	WANG D H,ZHANG Y,ZHAO Y. LightGBM:an effective miRNA classification method in breast cancer patients[C]//Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics. Newark NJ, USA:ACM,2017:7
[38]	TURNER R,ERIKSSON D,MCCOURT M,et al. Bayesian optimization is superior to random search for machine learning hyperparameter tuning:analysis of the black-box optimization challenge 2020[C]//NeurIPS 2020 Competition and Demonstration Track. PMLR:Semantic Scholar,2021
[39]	SAZANOVICH M,NIKOLSKAYA A,BELOUSOV Y,et al. Solving black-box optimization challenge via learning search space partition for local Bayesian optimization[C]//NeurIPS 2020 Competition and Demonstration Track. PMLR:Semantic Scholar,2020
[40]	LUNDBERG S M,LEE S I. An unexpected unity among methods for interpreting model predictions[J]. arXiv preprint arXiv:1611.07478,2016
[41]	SLACK D,HILGARD S,JIA E,et al. Fooling LIME and SHAP:adversarial attacks on post hoc explanation methods[C]//Proceedings of the AAAI/ACM Conference on AI,Ethics,and Society. New York, USA:ACM,2020:180
[42]	LUNDBERG S M,LEE S I. A unified approach to interpreting model predictions[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Red Hook,NY,USA:Curran Associates Inc,2017
[43]	KALANTAR B,PRADHAN B,NAGHIBI S A,et al. Assessment of the effects of training data selection on the landslide susceptibility mapping:a comparison between support vector machine (SVM),logistic regression (LR) and artificial neural networks (ANN)[J]. Geomatics,Natural Hazards and Risk,2018,9(1):49 doi: 10.1080/19475705.2017.1407368
[44]	LI X Y,GENG T,SHEN W J,et al. Quantifying the influencing factors and multi-factor interactions affecting cadmium accumulation in limestone-derived agricultural soil using random forest (RF) approach[J]. Ecotoxicology and Environmental Safety,2021,209:111773 doi: 10.1016/j.ecoenv.2020.111773
[45]	WANG D,THUNÉLL S,LINDBERG U,et al. Towards better process management in wastewater treatment plants:process analytics based on SHAP values for tree-based machine learning methods[J]. Journal of Environmental Management,2022,301:113941 doi: 10.1016/j.jenvman.2021.113941
[46]	冯兰茜. 黄土塬区沟坡稳定性评估及失稳机制研究[D]. 北京:中国科学院大学,2023
[47]	杨涛,李明俐,孙东,等. 黄土高陡塬坡降雨侵蚀机制及生态防治技术[J]. 科学技术与工程,2022,22(32):14518
[48]	周成虎,程维明,钱金凯,等. 中国陆地 1∶ 100 万数字地貌分类体系研究[J]. 地球信息科学学报,2009,11(6):707
[49]	殷跃平,李滨,张田田,等. 印度查莫利“2·7”冰岩山崩堵江溃决洪水灾害链研究[J]. 中国地质灾害与防治学报,2021,32(3):1
[50]	彭建兵,王启耀,庄建琦,等. 黄土高原滑坡灾害形成动力学机制[J]. 地质力学学报,2020,26(5):714
[51]	黄晓维. 中国致命性滑坡和黄土高原滑坡的灾害与风险评价[D]. 兰州:兰州大学,2019

Cited By

Get Citation

PDF

XML

Article Metrics

Article views (84) PDF downloads (11)

Turn off MathJax

Article Contents

Abstract

References

Quantitative assessment of landslide hazard susceptibility and key driving factors in loess plateau geomorphologic area

Abstract

References

Catalog

Article Metrics

Export File

Citation

Format

Content