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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
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    References listed on IDEAS

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    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.
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