Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 28, Issue 8, Pages 1957-1972Publisher
SPRINGER
DOI: 10.1007/s00477-014-0875-6
Keywords
Landslide displacement prediction; Extreme learning machine; Artificial neural networks; Global positioning system
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Funding
- Natural Science Foundation of China [61125303, 61203286]
- National Basic Research Program of China (973 Program) [2011CB710606]
- Program for Science and Technology in Wuhan of China [2014010101010004]
- Program for Changjiang Scholars and Innovative Research Team in University of China [IRT1245]
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Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD-ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD-ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.
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