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Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine

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  • Cheng Lian
  • Zhigang Zeng
  • Wei Yao
  • Huiming Tang

Abstract

In this paper, an M–EEMD–ELM model (modified ensemble empirical mode decomposition (EEMD)-based extreme learning machine (ELM) ensemble learning paradigm) is proposed for landslide displacement prediction. The nonlinear original surface displacement deformation monitoring time series of landslide is first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data structure. Then, these sub-series except the high frequency are forecasted, respectively, by establishing appropriate ELM models. At last, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original landslide displacement series. A case study of Baishuihe landslide in the Three Gorges reservoir area of China is presented to illustrate the capability and merit of our model. Empirical results reveal that the prediction using M–EEMD–ELM model is consistently better than basic artificial neural networks (ANNs) and unmodified EEMD–ELM in terms of the same measurements. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Cheng Lian & Zhigang Zeng & Wei Yao & Huiming Tang, 2013. "Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine," 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. 66(2), pages 759-771, March.
    • Handle: RePEc:spr:nathaz:v:66:y:2013:i:2:p:759-771
    DOI: 10.1007/s11069-012-0517-6
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    References listed on IDEAS

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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. G. Msilimba, 2010. "The socioeconomic and environmental effects of the 2003 landslides in the Rumphi and Ntcheu Districts (Malawi)," 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. 53(2), pages 347-360, May.
    3. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    4. Giuseppe Sorbino & Carlo Sica & Leonardo Cascini, 2010. "Susceptibility analysis of shallow landslides source areas using physically based models," 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. 53(2), pages 313-332, May.
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    Cited by:

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    2. Jyotirmayee Behera & Ajit Kumar Pasayat & Harekrushna Behera, 2022. "COVID-19 Vaccination Effect on Stock Market and Death Rate in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(4), pages 651-673, December.
    3. Elivelto Ebermam & Helder Knidel & Renato A. Krohling, 2022. "Development of a hybrid method for stock trading based on TOPSIS, EMD and ELM," Papers 2206.06723, arXiv.org.
    4. Yuting Liu & Giordano Teza & Lorenzo Nava & Zhilu Chang & Min Shang & Debing Xiong & Simonetta Cola, 2024. "Deformation evaluation and displacement forecasting of baishuihe landslide after stabilization based on continuous wavelet transform and deep learning," 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(11), pages 9649-9673, September.
    5. Xiaobo Liu & Lei Yang & Xingfan Zhang, 2019. "A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm," Energies, MDPI, vol. 12(5), pages 1-15, March.
    6. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.

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