IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v147y2021ics0960077921003234.html
   My bibliography  Save this article

A new grey prediction model and its application in landslide displacement prediction

Author

Listed:
  • Li, Shaohong
  • Wu, Na

Abstract

Developing a grey prediction model with high nonlinear prediction accuracy is an important issue in grey system theory. A new grey prediction model was developed that was the first to combine the idea of twin support vector regression with Hausdorff derivative operator. The new model is a non-linear data-driven model. An improved salp swarm algorithm is used to determine parameters of the model. Two numerical examples show that the error of the new model is smaller than the existing grey prediction models and least square support vector machine model. Moreover, with the displacement, precipitation, reservoir level elevation, variation velocity of reservoir level elevation, and displacement velocity of the previous month as the input variables, the new model was successfully used to predict the displacement of a landslide in the real-world. The new model is a powerful tool for solving nonlinear prediction problems.

Suggested Citation

  • Li, Shaohong & Wu, Na, 2021. "A new grey prediction model and its application in landslide displacement prediction," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:chsofr:v:147:y:2021:i:c:s0960077921003234
    DOI: 10.1016/j.chaos.2021.110969
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077921003234
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2021.110969?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Xiuzhen Li & Jiming Kong & Zhenyu Wang, 2012. "Landslide displacement prediction based on combining method with optimal weight," 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. 61(2), pages 635-646, March.
    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. Zian Lin & Yuanfa Ji & Weibin Liang & Xiyan Sun, 2022. "Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model," Mathematics, MDPI, vol. 10(13), pages 1-19, June.
    2. Zian Lin & Yuanfa Ji & Xiyan Sun, 2023. "Landslide Displacement Prediction Based on CEEMDAN Method and CNN–BiLSTM Model," Sustainability, MDPI, vol. 15(13), pages 1-20, June.

    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. Weidong Wang & Jiaying Li & Xia Qu & Zheng Han & Pan Liu, 2019. "Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in 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. 96(3), pages 1121-1139, April.
    2. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).
    3. Liulei Bao & Guangcheng Zhang & Xinli Hu & Shuangshuang Wu & Xiangdong Liu, 2021. "Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function," Energies, MDPI, vol. 14(4), pages 1-24, February.
    4. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).
    5. Hong Wang & Guangyu Long & Jianxing Liao & Yan Xu & Yan Lv, 2022. "A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement," 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. 111(2), pages 1479-1505, March.
    6. Wu, Wen-Ze & Zeng, Liang & Liu, Chong & Xie, Wanli & Goh, Mark, 2022. "A time power-based grey model with conformable fractional derivative and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    7. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
    8. Li, Xuemei & Wu, Xinran & Zhao, Yufeng, 2023. "Research and application of multi-variable grey optimization model with interactive effects in marine emerging industries prediction," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    9. Wang, Yong & Yang, Zhongsen & Ye, Lingling & Wang, Li & Zhou, Ying & Luo, Yongxian, 2023. "A novel self-adaptive fractional grey Euler model with dynamic accumulation order and its application in energy production prediction of China," Energy, Elsevier, vol. 265(C).
    10. He, Jing & Mao, Shuhua & Kang, Yuxiao, 2023. "Augmented fractional accumulation grey model and its application: Class ratio and restore error perspectives," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 220-247.
    11. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
    12. Ding, Song & Hu, Jiaqi & Lin, Qianqian, 2023. "Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model," Energy Economics, Elsevier, vol. 126(C).
    13. Xiaoyang Yu & Cheng Lian & Yixin Su & Bingrong Xu & Xiaoping Wang & Wei Yao & Huiming Tang, 2022. "Selective ensemble deep bidirectional RVFLN for landslide displacement prediction," 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. 112(1), pages 725-745, May.
    14. Yang, Yang & Wang, Xiuqin, 2022. "A novel modified conformable fractional grey time-delay model for power generation prediction," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    15. Xinchang Liu & Bolong Liu, 2023. "A Hybrid Time Series Model for Predicting the Displacement of High Slope in the Loess Plateau Region," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    16. Xu, Jie & Wu, Wen-Ze & Liu, Chong & Xie, Wanli & Zhang, Tao, 2024. "An extensive conformable fractional grey model and its application," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    17. Zian Lin & Xiyan Sun & Yuanfa Ji, 2022. "Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    18. Zian Lin & Yuanfa Ji & Weibin Liang & Xiyan Sun, 2022. "Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model," Mathematics, MDPI, vol. 10(13), pages 1-19, June.
    19. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    20. Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).

    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:eee:chsofr:v:147:y:2021:i:c:s0960077921003234. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.