NDLI: Rainfall-induced landslide displacement prediction model based on attention mechanism neural network

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Rainfall-induced landslide displacement prediction model based on attention mechanism neural network

Content Provider	Scilit
Author	Tang, Tianjun Tang, Feifei Wang, Xifei
Copyright Year	2021
Description	Journal: Iop Conference Series: Earth and Environmental Science Being influenced by fluctuating precipitation, long-term rainfall-induced landslide displacement prediction could be unstable. To solve this problem, a rainfall-induced landslide displacement prediction model based on Attention Mechanism Neural Network(AMNN) is proposed in this paper. Firstly, accumulative landslide displacement is decomposed into the trend term and the periodic term. Secondly, multivariate linear regression is adopted to fit the trend term and AMNN is used to predict the periodic term. Finally, the accumulative predicted displacement is given by summing the trend and the periodic displacement components. In this paper, one rainfall-induced landslide in Chongqing province, China was taken as an example to evaluate the designed model. Compared with some existing methods, the model proposed in this paper can captured the correlation between each feature sequence and the predicted term with higher prediction accuracy of 0.97 Goodness of Fit. The results of this paper are believed to be contributive to further rainfall-induced landslide forecast and early warning research.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/643/1/012137/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/643/1/012137
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	1
Volume Number	643
Language	English
Publisher	IOP Publishing
Publisher Date	2021-01-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Geological Engineering Induced Landslide Landslide Displacement Displacement Prediction
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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