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Owing to the complex formation mechanism of hydrodynamic landslides and the involvement of multiple influencing factors, the accuracy of the current prediction model of hydrodynamic landslide evolution process is unsatisfactory. This limitation prevents adequate monitoring and early warning on possible landslides in an area. To improve the accuracy of prediction model of hydrodynamic landslide evolution process supported by monitoring big data, the variational mode decomposition (VMD) and support vector regression (SVR) based on deep learning were integrated in the present study. Typical hydrodynamic landslide in the Three Gorges Reservoir Area (TGRA) in China is used as a case study for landslide displacement prediction. First, the VMD was utilized to decompose the cumulative displacement into the trend, periodic, and random terms. Then, external factors were decomposed into subsequences, and those characterized by periodicity and randomness were selected as input datasets. The associated displacement terms were then predicted using the Random Search–Support Vector Regression model. Finally, the total displacement was obtained by superimposing the three predicted components, and this was used to evaluate the performance of the model. The results show that the model improves the performance and accuracy of predicting the displacement associated with a hydrodynamic landslide, and the relative error is ≤2%.

A Prediction Model of Hydrodynamic Landslide Evolution Process Based on Deep Learning Supported by Monitoring Big Data