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Volume 34 Issue 6
Dec.  2023
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GAO Ziyan，LI Ruidong，SHI Pengqing，et al. Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network[J]. The Chinese Journal of Geological Hazard and Control，2023，34(6): 30-36. DOI: 10.16031/j.cnki.issn.1003-8035.202303062
Citation: GAO Ziyan，LI Ruidong，SHI Pengqing，et al. Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network[J]. The Chinese Journal of Geological Hazard and Control，2023，34(6): 30-36. DOI: 10.16031/j.cnki.issn.1003-8035.202303062
shu

Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network

  • 1.

    Key Laboratory of Groundwater Engineering and Geothermal Resources in Gansu Province, Lanzhou, Gansu　730050, China

  • 2.

    Geological Disaster Prevention and Control Technology Guidance Center, Gansu Provincial Department of Natural Resources, Lanzhou, Gansu　730050, China

  • 3.

    Gansu Institute of Geological Environment Monitoring, Lanzhou, Gansu　730050, China

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  • Received Date: March 26, 2023
  • Revised Date: September 26, 2023
  • Available Online: November 07, 2023
    Graphical Abstract
    • Abstract
  • The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error , mean absolute error , coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie.
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    • References (16)
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