IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i12p5522-d1679805.html

Landslide Risk Assessment Along Railway Lines Using Multi-Source Data: A GameTheory-Based Integrated Weighting Approach for Sustainable Infrastructure Planning

Author

Listed:
  • Yuqiang He

    (CHN Energy Shuohuang Railway Development Co., Cangzhou 062350, China)

  • Ziyan Bin

    (School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China)

  • Xiaolei Xu

    (China Academy of Railway Sciences Co., Ltd., Beijing 100081, China)

  • Hongsheng Yu

    (China Academy of Railway Sciences Co., Ltd., Beijing 100081, China)

  • Yan Zhang

    (China Academy of Railway Sciences Co., Ltd., Beijing 100081, China)

  • Na Li

    (CHN Energy Shuohuang Railway Development Co., Cangzhou 062350, China)

  • Man Li

    (School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China)

Abstract

Landslides threaten railway safety and operational sustainability. This study developed a game theory-based weighting method that integrates the Entropy Weight Method (EWM) and CRITIC with Analytic Hierarchy Process (AHP) techniques to determine indicator weights, reducing single-method biases. A risk assessment was conducted that coupled hazard likelihood with exposure. These components formed a comprehensive risk index visualized as a landslide risk map. A GIS-integrated assessment of Shandong Province railways incorporated multi-source data to support resilient infrastructure planning. The results show that high-risk zones consistently coincide with mountainous terrain, high-precipitation areas, and concentration of the population/economic activity, identifying critical intervention areas. The integrated weighting method proves effective for multi-criteria risk analysis. Decision-makers can prioritize mitigation measures using these insights, enhancing railway resilience and reducing regional disaster risk.

Suggested Citation

  • Yuqiang He & Ziyan Bin & Xiaolei Xu & Hongsheng Yu & Yan Zhang & Na Li & Man Li, 2025. "Landslide Risk Assessment Along Railway Lines Using Multi-Source Data: A GameTheory-Based Integrated Weighting Approach for Sustainable Infrastructure Planning," Sustainability, MDPI, vol. 17(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5522-:d:1679805
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/12/5522/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/12/5522/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yifei Zhu & Xin Yao & Leihua Yao & Chuangchuang Yao, 2022. "Detection and characterization of active landslides with multisource SAR data and remote sensing in western Guizhou, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 973-994, March.
    2. Xiaojie Yang & Zhenli Hao & Keyuan Liu & Zhigang Tao & Guangcheng Shi, 2023. "An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping," Sustainability, MDPI, vol. 15(7), pages 1-28, April.
    3. Qing Lan & Jianbo Tang & Xiaoming Mei & Xuexi Yang & Qinghao Liu & Qian Xu, 2023. "Hazard Assessment of Rainfall–Induced Landslide Considering the Synergistic Effect of Natural Factors and Human Activities," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    4. Ahmed Cemiloglu & Licai Zhu & Agab Bakheet Mohammednour & Mohammad Azarafza & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm," Land, MDPI, vol. 12(7), pages 1-20, July.
    5. Wei Xie & Wen Nie & Pooya Saffari & Luis F. Robledo & Pierre-Yves Descote & Wenbin Jian, 2021. "Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 931-948, October.
    6. Huimin Liu & Qixuan Ding & Xuexi Yang & Qinghao Liu & Min Deng & Rong Gui, 2024. "A Knowledge-Guided Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network and Graph Contrastive Learning," Sustainability, MDPI, vol. 16(11), pages 1-22, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saeed Alqadhi & Javed Mallick & Meshel Alkahtani & Intikhab Ahmad & Dhafer Alqahtani & Hoang Thi Hang, 2024. "Developing a hybrid deep learning model with explainable artificial intelligence (XAI) for enhanced landslide susceptibility modeling and management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(4), pages 3719-3747, March.
    2. Cong Quan Nguyen & Duc Anh Nguyen & Hieu Trung Tran & Thanh Trung Nguyen & Bui Thi Phuong Thao & Nguyen Tien Cong & Tran Phong & Hiep Le & Indra Prakash & Binh Thai Pham, 2025. "Predicting landslide and debris flow susceptibility using Logitboost alternating decision trees and ensemble techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(2), pages 1661-1686, January.
    3. Yimin Mao & He Qin & Shang Yaojun & Huang Zilong & Gao Zhaohui & Miao Decheng & Mehdi Kouhdaragh, 2025. "Implementing an explored advanced and integrated deep random forest learning-based model to monitor the enhanced landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(13), pages 15655-15677, July.
    4. Yanli Wang & Yaser A. Nanehkaran, 2024. "GIS-based fuzzy logic technique for mapping landslide susceptibility analyzing in a coastal soft rock zone," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 10889-10921, September.
    5. Chuhan Wang & Qigen Lin & Leibin Wang & Tong Jiang & Buda Su & Yanjun Wang & Sanjit Kumar Mondal & Jinlong Huang & Ying Wang, 2022. "The influences of the spatial extent selection for non-landslide samples on statistical-based landslide susceptibility modelling: a case study of Anhui Province in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 1967-1988, July.
    6. Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    7. Yang Chen & Majid Amani-Beni & Laleh Dehghanifarsani, 2025. "Multi-Scenario Simulation of Land Use/Land Cover Change in a Mountainous and Eco-Fragile Urban Agglomeration: Patterns and Implications," Land, MDPI, vol. 14(9), pages 1-27, September.
    8. Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motirh Al-Mutiry, 2022. "GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    9. Mohammed J. Alshayeb & Hoang Thi Hang & Ahmed Ali A. Shohan & Ahmed Ali Bindajam, 2024. "Novel optimized deep learning algorithms and explainable artificial intelligence for storm surge susceptibility modeling and management in a flood-prone island," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(6), pages 5099-5128, April.
    10. Guoyi Li & Weiwei Shao & Xin Su & Yong Li & Yi Zhang & Tianxu Song, 2025. "Urban Flood Hazard Assessment Based on Machine Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(5), pages 1953-1970, March.
    11. Xiaojie Geng & Shunchuan Wu & Yanjie Zhang & Junlong Sun & Haiyong Cheng & Zhongxin Zhang & Shijiang Pu, 2023. "Developing hybrid XGBoost model integrated with entropy weight and Bayesian optimization for predicting tunnel squeezing intensity," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 751-771, October.
    12. Tingyu Zhang & Quan Fu & Chao Li & Fangfang Liu & Huanyuan Wang & Ling Han & Renata Pacheco Quevedo & Tianqing Chen & Na Lei, 2022. "Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3327-3358, December.
    13. Yuto Omae, 2023. "Effects of Exploration Weight and Overtuned Kernel Parameters on Gaussian Process-Based Bayesian Optimization Search Performance," Mathematics, MDPI, vol. 11(14), pages 1-13, July.
    14. Fatiha Debiche & Mohammed Amin Benbouras & Alexandru-Ionut Petrisor & Lyes Mohamed Baba Ali & Abdelghani Leghouchi, 2024. "Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach," Land, MDPI, vol. 13(6), pages 1-29, June.
    15. Muhammad Muzamil Khan & Bushra Ghaffar & Rasim Shahzad & M. Riaz Khan & Munawar Shah & Ali H. Amin & Sayed M. Eldin & Najam Abbas Naqvi & Rashid Ali, 2022. "Atmospheric Anomalies Associated with the 2021 M w 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    16. Zefang Zhang & Zhikuan Qian & Yong Wei & Xing Zhu & Linjun Wang, 2022. "Evaluation of Geological Disaster Sensitivity in Shuicheng District Based on the WOE-RF Model," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    17. Xiaoning Zhao & Zhongcheng Wei & Yukun Gao & Penggang Yin, 2023. "Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    18. Yaohui Liu & Xinkai Wang & Jie Zhou & Huaqiao Xing & Tongwen Liu & Mengqiao He, 2026. "Structure-driven evaluation of GBDT and neural networks for Geo-Tabular landslide hazard assessment on the Tibetan plateau," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(6), pages 1-36, March.
    19. Lei Yang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Belgacem Bouallegue & Muhammad Faisal Javed & Nermin M. Salem, 2022. "Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestrians," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    20. Sandra Lucia Cobos-Mora & Victor Rodriguez-Galiano & Luigi Lombardo, 2026. "Displacement prediction of slow-moving landslides using InSAR and ensemble regression models based on slope units," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(5), pages 1-36, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5522-:d:1679805. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.