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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.

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