A algorithm on slope deformation prediction based on the Chaos-LSTM deep learning network
To achieve precise prediction and timely stability assessment of slope deformations in opencast mines,based on the time series data of slope deformations,the optimal embedding dimension and time delay for phase space reconstruction of the time series were determined by the mutual information method and the cross-correlation(C-C)method.Through phase space reconstruction,the dynamic evolution law of the nearest neighbor phase points in the time series of slope deformations was thoroughly analyzed.On this basis,the Long Short-Term Memory(LSTM)artificial neural network was combined to predict the time series of slope deformations.The results indicated that,under the condition of phase space reconstruction,an increase in the average distance between the nearest neighbor phase points signified more prominent chaotic characteristics of slope deformations and a more complex internal dynamic system.The LSTM deep learning network demonstrated high accuracy in short-term predictions,effectively predicting slope deformations.Based on the evolution law of the nearest neighbor phase points and the deformation prediction results,the stability state of the slope can be accurately identified,providing new ideas and methods for slope stability assessment during opencast mining.
NETL
NSTL
万方数据
