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

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.