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Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change.

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Title: Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change.
Authors: Han, Heming1 (AUTHOR) hanheming@smail.nju.edu.cn, Shi, Bin1 (AUTHOR) shibin@nju.edu.cn, Zhang, Lei1 (AUTHOR)
Source: Engineering Geology. Jan2021, Vol. 280, pN.PAG-N.PAG. 1p.
Subjects: Water table, Landslide hazard analysis, Landslide prediction, Standard deviations, Hysteresis, Groundwater, Particle swarm optimization
Abstract: The displacement-time curves of reservoir landslide mostly show step-by-step growth characteristics. The sharp increase of the displacement plays a significant role in the evolution process of the step-like landslide, so it is extremely important to accurately predict the suddenly change displacement of landslide. In order to overcome the shortcomings existed in the current displacement monitoring methods and prediction models for the mutation displacement of landslide, this paper proposed a hybrid machine learning displacement prediction model based on Support Vector Machine, including Support Vector Classification(SVC) and Support Vector Regression(SVR), optimized by Particle Swarm Optimization (SVC-PSO-SVR) and considering the hysteresis of groundwater level change. The Majiagou No. 1 Landslide in the Three Gorges Reservoir (TGR) Area, whose deformation shows typical step-by-step tendency, was illustrated. Base on the deep displacement data of Majiagou landslide from January 2016 to December 2017, the landslide deformation pattern can be divided into two states: stability and acceleration. Firstly, the SVC model was adopted to predict the time range of acceleration state. Then, considering the lag fluctuation of groundwater level, the concept of "equivalent reservoir level" was proposed. Finally, based on SVC model, the sharp increase displacement of landslide was predicted using PSO-SVR model. The proposed SVC-PSO-SVR model yielded the root mean square error (RMSE) of 0.08827 mm and the mean absolute percentage error (MAPE) of 0.02105 mm. Besides, the prediction accuracy of SVC can attain 96.49% (55 / 57). The results show that the model can accurately predict the time range and displacement of acceleration deformation section of Majiagou landslide. The proposed model is of great significance for landslide prediction and early warning. • The influence of hysteresis of groundwater change on landslide displacement prediction was considered. • The SVC model was used to predict the time range of accelerated deformation of landslide. • The TSVC-PSO-SVR mode was proposed to improve the displacement prediction accuracy of landslide sharp increase displacement. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Geology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change.
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  Data: <searchLink fieldCode="AR" term="%22Han%2C+Heming%22">Han, Heming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hanheming@smail.nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shi%2C+Bin%22">Shi, Bin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shibin@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lei%22">Zhang, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Geology%22">Engineering Geology</searchLink>. Jan2021, Vol. 280, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Water+table%22">Water table</searchLink><br /><searchLink fieldCode="DE" term="%22Landslide+hazard+analysis%22">Landslide hazard analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Landslide+prediction%22">Landslide prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Hysteresis%22">Hysteresis</searchLink><br /><searchLink fieldCode="DE" term="%22Groundwater%22">Groundwater</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The displacement-time curves of reservoir landslide mostly show step-by-step growth characteristics. The sharp increase of the displacement plays a significant role in the evolution process of the step-like landslide, so it is extremely important to accurately predict the suddenly change displacement of landslide. In order to overcome the shortcomings existed in the current displacement monitoring methods and prediction models for the mutation displacement of landslide, this paper proposed a hybrid machine learning displacement prediction model based on Support Vector Machine, including Support Vector Classification(SVC) and Support Vector Regression(SVR), optimized by Particle Swarm Optimization (SVC-PSO-SVR) and considering the hysteresis of groundwater level change. The Majiagou No. 1 Landslide in the Three Gorges Reservoir (TGR) Area, whose deformation shows typical step-by-step tendency, was illustrated. Base on the deep displacement data of Majiagou landslide from January 2016 to December 2017, the landslide deformation pattern can be divided into two states: stability and acceleration. Firstly, the SVC model was adopted to predict the time range of acceleration state. Then, considering the lag fluctuation of groundwater level, the concept of "equivalent reservoir level" was proposed. Finally, based on SVC model, the sharp increase displacement of landslide was predicted using PSO-SVR model. The proposed SVC-PSO-SVR model yielded the root mean square error (RMSE) of 0.08827 mm and the mean absolute percentage error (MAPE) of 0.02105 mm. Besides, the prediction accuracy of SVC can attain 96.49% (55 / 57). The results show that the model can accurately predict the time range and displacement of acceleration deformation section of Majiagou landslide. The proposed model is of great significance for landslide prediction and early warning. • The influence of hysteresis of groundwater change on landslide displacement prediction was considered. • The SVC model was used to predict the time range of accelerated deformation of landslide. • The TSVC-PSO-SVR mode was proposed to improve the displacement prediction accuracy of landslide sharp increase displacement. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Geology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.enggeo.2020.105876
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Water table
        Type: general
      – SubjectFull: Landslide hazard analysis
        Type: general
      – SubjectFull: Landslide prediction
        Type: general
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Hysteresis
        Type: general
      – SubjectFull: Groundwater
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
    Titles:
      – TitleFull: Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Han, Heming
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            NameFull: Shi, Bin
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            NameFull: Zhang, Lei
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          Dates:
            – D: 01
              M: 01
              Text: Jan2021
              Type: published
              Y: 2021
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              Value: 280
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            – TitleFull: Engineering Geology
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