IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v116y2023i2d10.1007_s11069-022-05748-3.html
   My bibliography  Save this article

Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks

Author

Listed:
  • Han Zhang

    (Shandong University of Technology)

  • Chao Yin

    (Shandong University of Technology)

  • Shaoping Wang

    (Rizhao City Construction Investment Group Co., Ltd.)

  • Bing Guo

    (Rizhao City Construction Investment Group Co., Ltd.)

Abstract

Based on landslide survey data and geological conditions of the research area, landslide susceptibility mapping is to analyze the impact of combination characteristics of hazard-inducing factors on the occurrence probability and divide the area into different susceptible areas. The engineering rock formation and geological hazards survey were conducted and established a landslide database of Boshan District. The 99 landslides in Boshan District were classified into 42 natural landslides and 57 engineering landslides, whose accuracy was validated by K-means cluster. The hazard-inducing factors of all landslides, natural landslides and engineering landslides were graded by the information value method and established a seven-layer improved convolutional neural networks. The model for the susceptibility assessment was trained and verified for all landslides, natural landslides and engineering landslides, whose accuracy had been validated by the AUC method. Based on ArcGIS12.0, the landslide susceptibility probability in Boshan District was calculated to have drawn a landslide susceptibility map. The results show that the landslide susceptibility probability had a minimum value of 0.136 and a maximum value of 0.841. Extreme, high, moderate, minor and minimal dangerous areas, respectively, accounted for 8.08% (56.4 km2), 17.62% (123.0 km2), 25.33% (176.8 km2), 32.87% (229.4 km2) and 16.10% (112.4 km2) of the total areas of Boshan District.

Suggested Citation

  • Han Zhang & Chao Yin & Shaoping Wang & Bing Guo, 2023. "Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks," 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(2), pages 1931-1971, March.
    • Handle: RePEc:spr:nathaz:v:116:y:2023:i:2:d:10.1007_s11069-022-05748-3
    DOI: 10.1007/s11069-022-05748-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05748-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-022-05748-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jules Maurice Habumugisha & Ningsheng Chen & Mahfuzur Rahman & Md Monirul Islam & Hilal Ahmad & Ahmed Elbeltagi & Gitika Sharma & Sharmina Naznin Liza & Ashraf Dewan, 2022. "Landslide Susceptibility Mapping with Deep Learning Algorithms," Sustainability, MDPI, vol. 14(3), pages 1-22, February.
    2. 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.
    3. Chao Yin & Zhanghua Wang & Xingkui Zhao, 2022. "Spatial prediction of highway slope disasters based on convolution neural networks," 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. 113(2), pages 813-831, September.
    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. Mohib Ullah & Bingzhe Tang & Wenchao Huangfu & Dongdong Yang & Yingdong Wei & Haijun Qiu, 2024. "Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region," Land, MDPI, vol. 13(7), pages 1-22, July.
    2. Shaohan Zhang & Shucheng Tan & Yongqi Sun & Duanyu Ding & Wei Yang, 2024. "Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)," Land, MDPI, vol. 13(9), pages 1-25, August.

    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. 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.
    2. 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.
    3. 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.
    4. Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    5. Mohammad Amin Khalili & Luigi Guerriero & Mostafa Pouralizadeh & Domenico Calcaterra & Diego Martire, 2023. "Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR 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. 119(1), pages 39-68, October.
    6. Cuiying Zhou & Jinwu Ouyang & Zhen Liu & Lihai Zhang, 2022. "Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    7. 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.
    8. Jae-Hyeon Park & Seong-Gyun Park & Hyun Kim, 2022. "Applicability Evaluation of Landslide Vulnerability Criteria for Decision on Landcreep-Vulnerable Areas in South Korea," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    9. 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.
    10. Lingfan Ju & Huan Yu & Qing Xiang & Wenkai Hu & Xiaoyu Xu, 2023. "Spatial Coupling Pattern and Driving Forces of Rural Settlements and Arable Land in Alpine Canyon Region of the Maoxian County, China," IJERPH, MDPI, vol. 20(5), pages 1-18, February.
    11. 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.
    12. Yadviga Tynchenko & Vladislav Kukartsev & Vadim Tynchenko & Oksana Kukartseva & Tatyana Panfilova & Alexey Gladkov & Van Nguyen & Ivan Malashin, 2024. "Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data," Sustainability, MDPI, vol. 16(16), pages 1-26, August.
    13. Gongfa Chen & Wei Deng & Mansheng Lin & Jianbin Lv, 2023. "Slope stability analysis based on convolutional neural network and digital twin," 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 1427-1443, September.
    14. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.

    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:spr:nathaz:v:116:y:2023:i:2:d:10.1007_s11069-022-05748-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.