IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5470-d1102500.html
   My bibliography  Save this article

Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Author

Listed:
  • Chenhui Wang

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Wei Guo

    (Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding 071051, China)

Abstract

Accurate prediction of landslide displacement is an effective way to reduce the risk of landslide disaster. Under the influence of periodic precipitation and reservoir water level, many landslides in the Three Gorges Reservoir area underwent significant displacement deformation, showing a similar step-like deformation curve. Given the nonlinear characteristics of landslide displacement, a prediction model is established in this study according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized by gray wolf optimizer (GWO-SVR). First, the original data are decomposed into trend, periodic and random components by VMD. Then, appropriate influential factors are selected using the grey relational degree analysis (GRDA) method for constructing the input training data set. Finally, the sum of the three displacement components is superimposed as the total displacement of the landslide, and the feasibility of the model is subsequently tested. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the accuracy of the model is verified using the long time-series monitoring data. The results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization. Therefore, this model can be used for the predict the accuracy of names and affiliations ion of landslide displacement in the Three Gorges Reservoir area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5470-:d:1102500
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5470/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5470/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, 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(1), pages 173-204, January.
    2. Qing Ling & Qin Zhang & Jing Zhang & Lingjie Kong & Weiqi Zhang & Li Zhu, 2021. "Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, 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. 108(1), pages 925-946, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Chenhui Wang & Gaocong Lin & Cuiqiong Zhou & Wei Guo & Qingjia Meng, 2024. "Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition," Land, MDPI, vol. 13(10), pages 1-17, October.
    3. Xuebin Xie & Yingling Huang, 2024. "Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model," Mathematics, MDPI, vol. 12(7), pages 1-17, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," 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. 120(13), pages 11485-11525, October.
    2. Ying Chen & Shirui Chen & Zhengyu Wu & Bing Dai & Longhua Xv & Guicai Wu, 2022. "Optimization of Genetic Algorithm through Use of Back Propagation Neural Network in Forecasting Smooth Wall Blasting Parameters," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
    3. Javed Mallick & Saeed Alqadhi & Swapan Talukdar & Majed AlSubih & Mohd. Ahmed & Roohul Abad Khan & Nabil Ben Kahla & Saud M. Abutayeh, 2021. "Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    4. Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," 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. 117(1), pages 189-205, May.
    5. Chenhui Wang & Gaocong Lin & Cuiqiong Zhou & Wei Guo & Qingjia Meng, 2024. "Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition," Land, MDPI, vol. 13(10), pages 1-17, October.
    6. Shidong Wu & Hengrui Ma & Abdullah M. Alharbi & Bo Wang & Li Xiong & Suxun Zhu & Lidong Qin & Gangfei Wang, 2023. "Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting," Sustainability, MDPI, vol. 15(20), pages 1-23, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5470-:d:1102500. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.