IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10180-d1180440.html

Analysis of Optimal Buffer Distance for Linear Hazard Factors in Landslide Susceptibility Prediction

Author

Listed:
  • Lu Fang

    (School of Earth Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, China
    School of Naval Architecture and Ocean Engineering, Jiangsu Maritime Institute, Nanjing 211199, China)

  • Qian Wang

    (Shandong Gold Group Penglai Mining Co., Ltd., Yantai 265621, China)

  • Jianping Yue

    (School of Earth Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, China)

  • Yin Xing

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

Abstract

A linear hazard-causing factor is the environmental element of landslide susceptibility prediction, and the setting of buffer distance of a linear hazard-causing factor has an important influence on the accuracy of landslide susceptibility prediction based on machine learning algorithms. A geographic information system (GIS) has generally been accepted in the correlation analysis between linear hazard-causing factors and landslides; the most common are statistical models based on buffer zone analysis and superposition analysis for linear causative factor distances and landslide counts. However, there is a problem in the process of model building: the buffer distance that is used to build the statistical model and its statistical results can appropriately reflect the correlation between the linear disaster-causing factors and landslides. To solve this problem, a statistical model of landslide density and distance of linear disaster-causing factors under different single-loop buffer distances was established based on Pearson’s method with 12 environmental factors, such as elevation, topographic relief, and distance from the water system and road, in Ruijin City, Jiangxi Province to obtain the most relevant single-loop buffer distance linear disaster-causing factor combinations; random forest (RF) machine learning models were then used to predict landslide susceptibility. Finally, the Kappa coefficient and the distribution characteristics of the susceptibility index were used to investigate the modeling laws. The analysis results indicate that the prediction accuracy of the most correlated single-loop buffer distance combination reaches 96.65%, the error rate of non-landslide points is 4.2%, and the error of landslide points is 11.3%, which is higher than the same single-loop buffer distance combination, confirming the reasonableness of the method of using correlation to obtain the linear disaster-causing factor buffer distance.

Suggested Citation

  • Lu Fang & Qian Wang & Jianping Yue & Yin Xing, 2023. "Analysis of Optimal Buffer Distance for Linear Hazard Factors in Landslide Susceptibility Prediction," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10180-:d:1180440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10180/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/10180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Faraz S. Tehrani & Michele Calvello & Zhongqiang Liu & Limin Zhang & Suzanne Lacasse, 2022. "Machine learning and landslide studies: recent advances and applications," 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(2), pages 1197-1245, November.
    2. Wei Lin & Kunlong Yin & Ningtao Wang & Yong Xu & Zizheng Guo & Yuanyao Li, 2021. "Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, 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. 106(1), pages 679-700, March.
    3. Weidong Wang & Zhuolei He & Zheng Han & Yange Li & Jie Dou & Jianling Huang, 2020. "Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, 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. 103(3), pages 3239-3261, September.
    4. Danang Sri Hadmoko & Franck Lavigne & Guruh Samodra, 2017. "Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia," 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. 87(1), pages 437-468, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Longye Hu & Chaode Yan, 2024. "Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model," Sustainability, MDPI, vol. 16(12), pages 1-23, June.

    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. Batmyagmar Dashbold & L. Sebastian Bryson & Matthew M. Crawford, 2023. "Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model," 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. 116(1), pages 235-265, March.
    2. Jiahui Dong & Ruiqing Niu & Tao Chen & LiangYun Dong, 2024. "Assessing landslide susceptibility using improved machine learning methods and considering spatial heterogeneity for the Three Gorges Reservoir Area, 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. 120(2), pages 1113-1140, January.
    3. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," 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(13), pages 11485-11525, October.
    4. Mehdi Bashiri & Mohammad Reza Rahdari & Francisco Serrano-Bernardo & Jesús Rodrigo-Comino & Andrés Rodríguez-Seijo, 2025. "Defining a Method for Mapping Aeolian Sand Transport Susceptibility Using Bivariate Statistical and Machine Learning Methods—A Case Study of the Seqale Watershed, Eastern Iran," Sustainability, MDPI, vol. 17(18), pages 1-22, September.
    5. Zohre Hoseinzade & Asal Zavarei & Kourosh Shirani, 2021. "Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation," 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(3), pages 2489-2507, December.
    6. Rajitha Sachinthaka & Roohollah Kalatehjari & Martin S. Brook, 2025. "Evolution and critical evaluation of deterministic physically based rainfall-induced landslide susceptibility mapping: a mixed review," 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(18), pages 20795-20818, November.
    7. Mustafa Kamal & Baolei Zhang & Jianfei Cao & Xin Zhang & Jun Chang, 2022. "Comparative Study of Artificial Neural Network and Random Forest Model for Susceptibility Assessment of Landslides Induced by Earthquake in the Western Sichuan Plateau, China," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    8. Chi Yang & Jinghan Wang & Shuyi Li & Ruihan Xiong & Xiaobo Li & Lin Gao & Xu Guo & Chuanming Ma & Hanxiang Xiong & Yang Qiu, 2024. "Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China," Sustainability, MDPI, vol. 16(11), pages 1-26, May.
    9. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    10. Qing Liu & Tingting Wu & Yahong Deng & Zhiheng Liu, 2023. "SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function," Land, MDPI, vol. 12(8), pages 1-19, July.
    11. Xianmin Wang & Xinlong Zhang & Jia Bi & Xudong Zhang & Shiqiang Deng & Zhiwei Liu & Lizhe Wang & Haixiang Guo, 2022. "Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning," IJERPH, MDPI, vol. 19(21), pages 1-26, October.
    12. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    13. Xianyu Yu & Tingting Xiong & Weiwei Jiang & Jianguo Zhou, 2023. "Comparative Assessment of the Efficacy of the Five Kinds of Models in Landslide Susceptibility Map for Factor Screening: A Case Study at Zigui-Badong in the Three Gorges Reservoir Area, China," Sustainability, MDPI, vol. 15(1), pages 1-26, January.
    14. Nilesh Suresh Pawar & Kul Vaibhav Sharma, 2025. "Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions," 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(20), pages 23687-23721, December.
    15. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    16. Shengjie Rui & Zhen Guo & Wenjie Zhou, 2023. "Promoting Sustainable Marine Development: Geotechnical Engineering Problems and Environmental Guarantee Technology in Marine Space, Energy, and Resource Development," Sustainability, MDPI, vol. 15(19), pages 1-3, October.
    17. Şevki Öztürk, 2025. "Comparative landslide susceptibility mapping using local inventories: a case study from Trabzon, Türkiye," 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(12), pages 14655-14676, July.
    18. Prahlada V. Mittal & Rishabh Bafna & Ankush Mittal, 2023. "Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery," 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. 118(2), pages 1619-1643, September.
    19. Yuting Liu & Giordano Teza & Lorenzo Nava & Zhilu Chang & Min Shang & Debing Xiong & Simonetta Cola, 2024. "Deformation evaluation and displacement forecasting of baishuihe landslide after stabilization based on continuous wavelet transform and deep learning," 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(11), pages 9649-9673, September.
    20. Dian Nuraini Melati & Astisiasari Astisiasari & Raditya Panji Umbara & Wisyanto Wisyanto & Sukristiyanti Sukristiyanti & Yukni Arifianti & Syakira Trisnafiah & Trinugroho Trinugroho & Taufik Iqbal Ram, 2025. "Quantitative landslide risk analysis with limited dataset by leveraging geospatial approaches in Indonesia," 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(17), pages 20453-20487, October.

    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:15:y:2023:i:13:p:10180-:d:1180440. 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.