IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i13p4788-d379897.html
   My bibliography  Save this article

Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique

Author

Listed:
  • Junwei Ma

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Xiao Liu

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Xiaoxu Niu

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Yankun Wang

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Tao Wen

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • Junrong Zhang

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Zongxing Zou

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

Abstract

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

Suggested Citation

  • Junwei Ma & Xiao Liu & Xiaoxu Niu & Yankun Wang & Tao Wen & Junrong Zhang & Zongxing Zou, 2020. "Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4788-:d:379897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/13/4788/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/13/4788/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Junwei Ma & Xiaoxu Niu & Huiming Tang & Yankun Wang & Tao Wen & Junrong Zhang, 2020. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach," Complexity, Hindawi, vol. 2020, pages 1-15, January.
    2. Chen, Kunlong & Jiang, Jiuchun & Zheng, Fangdan & Chen, Kunjin, 2018. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning," Energy, Elsevier, vol. 150(C), pages 49-60.
    3. Yankun Wang & Huiming Tang & Tao Wen & Junwei Ma, 2020. "Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines," Complexity, Hindawi, vol. 2020, pages 1-15, May.
    4. Xie Hu & Roland Bürgmann & William H. Schulz & Eric J. Fielding, 2020. "Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    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. Zongxing Zou & Sha Lu & Fei Wang & Huiming Tang & Xinli Hu & Qinwen Tan & Yi Yuan, 2020. "Application of Well Drainage on Treating Seepage-Induced Reservoir Landslides," IJERPH, MDPI, vol. 17(17), pages 1-20, August.
    2. Emily Ying Yang Chan & Holly Ching Yu Lam, 2021. "Research in Health-Emergency and Disaster Risk Management and Its Potential Implications in the Post COVID-19 World," IJERPH, MDPI, vol. 18(5), pages 1-3, 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. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    2. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    3. Hadjout, D. & Torres, J.F. & Troncoso, A. & Sebaa, A. & Martínez-Álvarez, F., 2022. "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market," Energy, Elsevier, vol. 243(C).
    4. Fasheng Miao & Yiping Wu & Linwei Li & Kang Liao & Yang Xue, 2021. "Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods," 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. 105(3), pages 2677-2696, February.
    5. Su-Ping Liu & Bin Shi & Kai Gu & Cheng-Cheng Zhang & Jian-Hui He & Jing-Hong Wu & Guang-Qing Wei, 2021. "Fiber-optic wireless sensor network using ultra-weak fiber Bragg gratings for vertical subsurface deformation monitoring," 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. 109(3), pages 2557-2573, December.
    6. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    7. Lixin Cheng & Qiuhua Tang & Zikai Zhang & Shiqian Wu, 2021. "Data mining for fast and accurate makespan estimation in machining workshops," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 483-500, February.
    8. Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
    9. Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
    10. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    11. Li, Yanying & Che, Jinxing & Yang, Youlong, 2018. "Subsampled support vector regression ensemble for short term electric load forecasting," Energy, Elsevier, vol. 164(C), pages 160-170.
    12. Sun, Chuanwang & Xu, Mengjie & Wang, Bo, 2024. "Deep learning: Spatiotemporal impact of digital economy on energy productivity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    13. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

    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:jijerp:v:17:y:2020:i:13:p:4788-:d:379897. 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.