Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

被引:83
作者
Lian, Cheng [1 ,2 ]
Zeng, Zhigang [1 ,2 ]
Yao, Wei [3 ]
Tang, Huiming [4 ]
机构
[1] Huazhong Univ Sci &, Sch Automat, Wuhan 430074, Peoples R China
[2] Minist Educ, Key Lab Image Informat Proc & Intelligent Control, Wuhan 430074, Peoples R China
[3] South Cent Univ Nationalities, Sch Comp Sci, Wuhan 430074, Peoples R China
[4] China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China
关键词
Landslide displacement prediction; Extreme learning machine; Artificial neural networks; Global positioning system; ARTIFICIAL NEURAL-NETWORKS; FEEDFORWARD NETWORKS; MUTUAL INFORMATION; HAZARD ASSESSMENT; TIME-SERIES; GPS; BACKPROPAGATION; MODEL;
D O I
10.1007/s00477-014-0875-6
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:1957 / 1972
页数:16
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