Abstract
Landslide displacement is widely obtained to discover landslide behaviors for purpose of event forecasting. This article aims to present a comparative study on landslide nonlinear displacement analysis and prediction using computational intelligence techniques. Three state-of-art techniques, the support vector machine (SVM), the relevance vector machine (RVM), and the Gaussian process (GP), are comparatively presented briefly for modeling landslide displacement series. The three techniques are discussed comparatively for both fitting and predicting the landslide displacement series. Two landslides, the Baishuihe colluvial landslide in China Three Georges and the Super-Sauze mudslide in the French Alps, are illustrated. The results prove that the computational intelligence approaches are feasible and capable of fitting and predicting landslide nonlinear displacement. The Gaussian process, on the whole, performs better than the support vector machine, relevance vector machine, and simple artificial neural network (ANN) with optimized parameter values in predictive analysis of the landslide displacement.
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Acknowledgments
Financial support from the China 973 Program for Key Basic Research Project (no. 2011CB013504) and the China Natural Science Foundation (no. 11272114) is gratefully acknowledged. The authors are also grateful to the anonymous reviewers for their comments and suggestions.
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Liu, Z., Shao, J., Xu, W. et al. Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides 11, 889–896 (2014). https://doi.org/10.1007/s10346-013-0443-z
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DOI: https://doi.org/10.1007/s10346-013-0443-z