A New Hybrid Method for Establishing Landslide Displacement Point Forecasting, Interval Forecasting and Probabilistic Forecasting
Abstract In addition to the inherent evolution trend, landslide displacement contains strong fluctuation and randomness, the omni-directional landslide displacement prediction is more scientific than single point prediction or interval prediction. In this work, a newly hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM) and gaussian process regression (GPR), was proposed for point, interval and probabilistic prediction of landslide displacement. The proposed model includes two parts: (i) predicting the inherent evolution trend of landslide displacement by DES-VMD-LSTM; (ii) evaluating the uncertainty in the first prediction based on the GPR model. In the first part, DES is used to predict the trend displacement, VMD is used to extract the periodic and stochastic displacement from the residual displacement, and then LSTM is used to predict them. The triggering factors of periodic and stochastic displacement are screened by maximum information coefficient (MIC), and the screened factors are decomposed into low- and high-frequency components by VMD, to predict periodic and stochastic displacement respectively. The first cumulative displacement prediction results are achieved by adding the predicted trend, periodic and stochastic displacement. By setting the first predicted displacement as input and actual displacement as expected output, the point, interval and probability prediction of displacement are realized in GPR model. The plausibility of this method was validated firstly with the data from Bazimen (BZM) and Baishuihe (BSH) landslide in the Three Gorges Reservoir area. This model has potential capacity to realize deterministic prediction of displacement and exhibit uncertainty contained in displacement. A comparing study shows that this method has a high performance at point, interval and probability prediction of displacement.