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- W3174141593 abstract "In landslide prediction, the deformation degree of landslide surface is predicted mainly through the prediction of GPS base stations on the landslide. In the landslide displacement series prediction research, the method combined with the prediction model is not considered to screen the inducing factors reasonably. Meanwhile, the parameter setting of the hidden layer is very uncertain in the landslide displacement prediction using the extreme learning machine. In this paper, firstly, the combined model of mean impact value (MIV) and extreme learning machine (MIV-ELM) are proposed to improve the quality of model input. Secondly, combined with Grey relational degree analysis, the number of hidden layer nodes of the extreme learning machine is determined reasonably and automatically, thus the Grey-ELM model is constructed. Thirdly, based on ELM and Grey-ELM, an integrated neural network is constructed by ensemble learning method. The root mean square prediction error of the integrated neural network is 10.93 mm, which is reduced by 25.1% compared with the E-ELM model, and the goodness of fit reaches 0.998. From the comparison of experimental results, the integrated neural network has certain stability and good generalization ability." @default.
- W3174141593 created "2021-07-05" @default.
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- W3174141593 date "2020-09-01" @default.
- W3174141593 modified "2023-10-17" @default.
- W3174141593 title "Landslide displacement prediction based on integrated neural network" @default.
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- W3174141593 doi "https://doi.org/10.1109/icdh51081.2020.00018" @default.
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