IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v113y2022i2d10.1007_s11069-022-05325-8.html
   My bibliography  Save this article

Spatial prediction of highway slope disasters based on convolution neural networks

Author

Listed:
  • Chao Yin

    (Shandong University of Technology)

  • Zhanghua Wang

    (Shandong Kezheng Project Management Co., LTD)

  • Xingkui Zhao

    (Shandong Dongtai Engineer Consulting Co., LTD)

Abstract

In order to clarify the spatial differentiations of highway slope disasters (HSDs) in Boshan District, spatial prediction was carried out based on ECG-CNN with the support of GIS. Spatial prediction factors of HSDs were selected, and the stabilities of the 147 highway slopes in Boshan District were determined. The spatial prediction model of HSDs was established by ECG-CNN, and the spatial susceptibility map of HSDs in Boshan District was plotted. Influences of the prediction factor combinations and the drill sample and verification sample combinations on the prediction success rates were verified. The results show that low susceptible areas, medium susceptible areas and high susceptible areas account for 56.92%, 28.46% and 14.62% of the total areas of Boshan District, respectively. Some sections of Binlai Expressway, G205, G309, S210 and S307, pass through high susceptible areas. The surface cutting depth has a small impact on the prediction success rate, while the elevation and gradient have great impacts on the prediction success rate. When the drill samples are small, network drill’s maturity has a great impact on the prediction success rate, while when there are many drill samples, the model’s logical structure itself has a large impact on the prediction success rate.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:113:y:2022:i:2:d:10.1007_s11069-022-05325-8
    DOI: 10.1007/s11069-022-05325-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05325-8
    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/s11069-022-05325-8?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. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    2. Chao Yin & Haoran Li & Fa Che & Ying Li & Zhinan Hu & Dong Liu, 2020. "Susceptibility mapping and zoning of highway landslide disasters in China," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    3. Chao Yin, 2020. "Hazard assessment and regionalization of highway flood disasters 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. 100(2), pages 535-550, January.
    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. Dinesh Kumar Malviya & Manojit Samanta & Rajesh Kumar Dash & Debi Prasanna Kanungo, 2024. "Anthropogenically induced instability in road cut slopes along NH-39, Manipur, North-East Indian Himalaya: Assessment and Mitigation Measures," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(3), pages 6239-6268, March.
    2. 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.

    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. Claire Murong Cui & Julie A. Harrison & Frederick Ng & Paul Rouse, 2025. "Accounting choices in data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 63(2), pages 219-237, April.
    2. Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    3. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    4. Sedghi, Sepideh & Azizi, Shima & Canada, Katherine & Charles, Vincent & Trapp, Andrew C., 2025. "Performance evaluation of child welfare departments using Data Envelopment Analysis: A comparative study across US states," Socio-Economic Planning Sciences, Elsevier, vol. 97(C).
    5. Gulati, Rachita & Charles, Vincent & Kumar, Sunil, 2024. "School education development index: A meta-frontier range directional measure benefit-of-the-doubt model," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    6. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    7. Chen, Chien-Ming & Wang, Hui, 2024. "Comparing eco-efficiency with productive efficiency: Addressing the dimensionality issue," European Journal of Operational Research, Elsevier, vol. 313(3), pages 1170-1179.
    8. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    9. Ebrahimi, Bohlool & Dhamotharan, Lalitha & Ghasemi, Mohammad Reza & Charles, Vincent, 2022. "A cross-inefficiency approach based on the deviation variables framework," Omega, Elsevier, vol. 111(C).
    10. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    11. Chao Yin & Haoran Li & Fa Che & Ying Li & Zhinan Hu & Dong Liu, 2020. "Susceptibility mapping and zoning of highway landslide disasters in China," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    12. Yang, Zijiang & Omrani, Hashem & Imanirad, Raha, 2024. "Assessing airline efficiency with a network DEA model: A Z-number approach with shared resources, undesirable outputs, and negative data," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    13. Milad Zamanifar & Timo Hartmann, 2020. "Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation 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. 104(1), pages 1-25, October.
    14. Bao Hoang Nguyen & Valentin Zelenyuk, 2021. "Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 123-158, Springer.
    15. Toloo, Mehdi & Keshavarz, Esmaeil & Hatami-Marbini, Adel, 2021. "Selecting data envelopment analysis models: A data-driven application to EU countries," Omega, Elsevier, vol. 101(C).
    16. Yongxiu He & Meiyan Wang & Fengtao Guang, 2019. "Applicability Evaluation of China’s Retail Electricity Price Package Combining Data Envelopment Analysis and a Cloud Model," Energies, MDPI, vol. 13(1), pages 1-21, December.
    17. Karagiannis, Roxani & Karagiannis, Giannis, 2023. "Nonparametric estimates of price efficiency for the Greek infant milk market: Curing the curse of dimensionality with shannon entropy," Economic Modelling, Elsevier, vol. 121(C).
    18. Filippo Visintin & Davide Aloini & Simone Gitto & Sara Vannelli & Daniele Feo, 2024. "Efficiency in radiology departments: a two-stage analysis," Flexible Services and Manufacturing Journal, Springer, vol. 36(2), pages 481-502, June.
    19. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeed Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    20. Olawale Ogunrinde & Ekundayo Shittu, 2023. "Benchmarking performance of photovoltaic power plants in multiple periods," Environment Systems and Decisions, Springer, vol. 43(3), pages 489-503, September.

    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:113:y:2022:i:2:d:10.1007_s11069-022-05325-8. 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.