Landslide Displacement Prediction Based on Variational ModeDecomposition and Deep Confidence Neural Network Model

HAN Fei; NIU Rui-qing; LI Shi-yao; ZHAO Ling-ran; BAI Xing-yu

doi:10.11988/ckyyb.20190638

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Journal of Changjiang River Scientific Research Institute ›› 2020, Vol. 37 ›› Issue (8) : 61-68. DOI: 10.11988/ckyyb.20190638

ENGINEERING SAFETY AND DISASTER PREVENTION

Landslide Displacement Prediction Based on Variational ModeDecomposition and Deep Confidence Neural Network Model

HAN Fei, NIU Rui-qing, LI Shi-yao, ZHAO Ling-ran, BAI Xing-yu

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Abstract

Frequent landslide disasters in the Three Gorges Reservoir area threaten the safety of people’s lives and property. Predicting landslide displacement rationally and effectively is of crucial significance for reducing property losses and protecting people’s lives. In view of the limits of conventional decomposition methods, the variational decomposition method which could control the number of decomposition modes was introduced into the decomposition of displacement time series. The Baijiabao landslide in the Three Gorges reservoir area was taken as a case study. The parameters are compared to improve the accuracy and effectiveness of the decomposition model. Moreover, a deep confidence network model involving landslide triggers was established to predict the displacement subsequences, and the results of all subsequences are reconstructed to obtain the total displacement prediction value. The mean average error of predicted total displacement is 3.657 mm, and the mean average percentage error is 0.010%, indicating a high accuracy.

Key words

landslide / variational mode decomposition / deep confidence neural networks / displacement prediction / error analysis

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HAN Fei, NIU Rui-qing, LI Shi-yao, ZHAO Ling-ran, BAI Xing-yu. Landslide Displacement Prediction Based on Variational ModeDecomposition and Deep Confidence Neural Network Model[J]. Journal of Changjiang River Scientific Research Institute. 2020, 37(8): 61-68 https://doi.org/10.11988/ckyyb.20190638

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