Abstract
Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior.
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Acknowledgments
The work is supported by the Natural Science Foundation of China under Grant 61125303 and 61203286, the 973 Program of China under Grant 2011CB710606, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 20100142110021.
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Lian, C., Zeng, Z., Yao, W. et al. Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis. Neural Comput & Applic 24, 99–107 (2014). https://doi.org/10.1007/s00521-013-1446-3
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DOI: https://doi.org/10.1007/s00521-013-1446-3