Abstract:A new method for paleo-landslide deformation prediction is proposed. Firstly, the paleo-landslide deformation data are decomposed by ensemble empirical mode decomposition(EEMD) and singular value decomposition(SVD), and then the deformation of the resurrected area of the paleo-landslide is predicted by the component combined neural network. Finally, the multi-fractal detrended fluctuation analysis(MF-DFA) is used to evaluate the multi-scale trend of the ancient landslide. Taking Wangjiapo landslide as an example, the effectiveness of the proposed method is analyzed.The results show that the combined decomposition model EEMD-SVD has stronger data decomposition ability than the single decomposition model which can effectively realize the information decomposition of landslide deformation data. The sub item combination prediction model based on neural network is suitable for landslide deformation prediction. The relative error of the prediction results is mostly about 2%, with high prediction accuracy. The extrapolation prediction shows that the landslide deformation will increase further, with an increase rate of 1.23-1.36 mm/cycle.Through the multi-scale feature analysis of MF-DFA model, we concluded that the landslide deformation has multifractal characteristics, and the deformation tends to increase, which is consistent with the prediction results, and proves the accuracy of the above prediction results.
TIAN Qian,WU Jian,ZHAO Dong. Deformation Prediction and Trend Evaluation of Paleo-Landslide Based on Neural Network and Multi-Scale Feature Analysis[J]. jgg, 2022, 42(10): 1056-1062.