A VMD-DES-TSAM-LSTM-based interpretability multi-step prediction approach for landslide displacement

Abstract

Obtaining reliable and high-accuracy prediction results of displacement trends in the long term is crucial for mitigating geohazards. Deep learning can capture dynamic and nonlinear characteristics in long-term time series and is widely used in landslide displacement prediction. However, its black-box attribute prevents decision-makers from understanding the basis of the model output, which limits the application of the model in the final optimization scenario. Thus, a novel “decomposition-prediction-addition-interpretative” framework including variational mode decomposition (VMD), double exponential smoothing (DES), and a long short-term memory network with spatiotemporal attention mechanism (TSAM-LSTM) is proposed. It enables high-precision multi-step prediction and spatiotemporal dimension interpretability analysis. Therein, VMD decomposes the total displacement into the trend, periodic, and random displacement, and the random displacement is further decomposed by VMD. On this basis, DES is used to predict the trend displacement, TSAM-LSTM to predict the periodic and random displacements, and finally, all the predicted values are superimposed to realize the total displacement prediction. The performance of the proposed approach was validated using monitoring data from H8 accumulation, Huangzangsi water conservancy project. The results indicate that the VMD-DES-TSAM-LSTM can achieve satisfactory prediction results. The introduction of TSAM improves the generalization ability of the traditional LSTM and significantly enhances its prediction performance. Meanwhile, TSAM accurately reveals the most relevant temporal and spatial information contained in input data that affects target displacement and visualizes the attention focus during model training, which provides a more favorable basis for model optimization and disaster prevention decision-making.