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

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...

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Bibliographic Details
Published in:Natural hazards (Dordrecht) 2013-03, Vol.66 (2), p.759-771
Main Authors: Lian, Cheng, Zeng, Zhigang, Yao, Wei, Tang, Huiming
Format: Article
Language:English
Subjects:
Artificial neural networks
Canyons
China (People's Republic)
Civil Engineering
Data structures
Decomposition
Deformation
Displacement
Earth and Environmental Science
Earth Sciences
Earth, ocean, space
Elm
Empirical analysis
Engineering and environment geology. Geothermics
Ensemble forecasting
Environmental Management
Exact sciences and technology
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
High frequency
Hydrogeology
Landslides
Learning
Learning algorithms
Machine learning
Mathematical models
Measurement
Natural Hazards
Natural hazards: prediction, damages, etc
Neural networks
Nonlinear systems
Original Paper
Prediction models
Reservoirs
Time series
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Summary: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.
ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-012-0517-6