Step-like displacement prediction of landslides guided by deformation mechanism
Rainfall reservoir-induced landslides in the Three Gorges Reservoir(TGR),China,exhibit distinctive step-like de-formation characteristics,involving mutation and creep states.These particular features pose a challenge for accurate early warning and prediction.Previous landslide displacement forecasting models have shown limited prediction accuracy,particular-ly when it comes to mutational displacements.The proposed prediction model in this study,based on Informer,utilizes a multi-head attention mechanism to capture temporal dependencies and incorporates pooling layers for emphasizing crucial fea-tures,enabling adaptive learning of feature weights and more effective extraction of periodic information from time series data.The Baishuihe landslide was used for case studies with monitoring data collected from July 2013 to December 2018,including monthly displacements,daily rainfall and reservoir water level.Firstly,cumulative displacement was decomposed into trend displacement and periodic displacement by the variational mode decomposition(VMD).After triggering factors selection and decomposition,the double exponential smoothing(DES)method and the Informer model are used to predict the trend and peri-odic component displacements,respectively.Finally,the predicted trend and periodic components are combined to generate the cumulative displacement prediction.Results demonstrate that the proposed model achieves impressive results with a root mean square error of 12.21 mm,a mean absolute error of 10.05 mm,and a coefficient of determination of 0.99 for the next 27 months'cumulative displacement prediction.Compared to other four mainstream models,this approach exhibits higher predic-tion accuracy,particularly in predicting the rapid deformation phase of step-like bank landslides.Consequently,it holds signifi-cant credibility and practical value in the early warning research of rainfall reservoir-induced landslides.
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