Displacement prediction model of landslide based on ensemble of extreme learning machine

Authors:
Cheng Lian;Zhigang Zeng;Wei Yao;Huiming Tang
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China,Key Laboratory of Image Processing and Intelligent Control of Education Ministry o ...;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China,Key Laboratory of Image Processing and Intelligent Control of Education Ministry o ...;School of Computer Science, South-Central University for Nationalities, Wuhan, Hubei, China;Faculty of Engineering, China University of Geosciences, Wuhan, Hubei, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

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Abstract

Based on time series analysis, total accumulative displacement of landslide is divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes of landslide displacement and inducing factors. In this paper, a novel neural network technique called the ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. 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. A case study of Baishuihe landslide in the Three Gorges reservoir area is presented to illustrate the capability and merit of our model.