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Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model

Author

Listed:
  • Longye Hu

    (School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China)

  • Chaode Yan

    (School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
    Yellow River Laboratory, Zhengzhou University, Zhengzhou 450001, China)

Abstract

The Mangshan Mountain is located in the south bank of the Yellow River, which belongs to the typical loess plateau. Landslide disasters occur frequently in this region, so it is urgent to carry out the evaluation of landslide susceptibility. Therefore, this study takes Mangshan Mountain as the research object, selects 13 evaluation factors through multicollinearity diagnostic, Pearson correlation coefficient, and random forest importance analysis, and uses grey wolf optimizer (GWO) algorithm to optimize the initial weights of one-dimensional convolutional neural network model (1D CNN), so as to build a GWO-1D CNN model to carry out the evaluation of landslide susceptibility. The results show that the GWO algorithm can significantly improve the accuracy of 1D CNN model. The final accuracy of the GWO-1D CNN model reaches 0.903, and the accuracy, area under the ROC curve, and kappa coefficients increase by 0.091, 0.098, and 0.187, respectively; The percentage of area of very low, low, medium, high, and very high susceptibility areas in Mangshan Mountain is 40.2%, 23.6%, 14.1%, 12.9%, and 9.2%. The findings of this study provide scientific basis for the prevention and control of landslide disaster in Mangshan Mountain and expand the application of CNN model in the evaluation of landslide susceptibility.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5086-:d:1415248
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    References listed on IDEAS

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    1. Jinxuan Zhou & Shucheng Tan & Jun Li & Jian Xu & Chao Wang & Hui Ye, 2023. "Landslide Susceptibility Assessment Using the Analytic Hierarchy Process (AHP): A Case Study of a Construction Site for Photovoltaic Power Generation in Yunxian County, Southwest China," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    2. 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.
    3. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    4. Chenhui Wang & Wei Guo, 2023. "Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
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