Wavelet-optimized InSAR monitoring subsidence prediction method using GRU-ARMA
This article proposes an optimization algorithm based on the wavelet gated recurrent neural network autoregressive moving average model(GRU-ARMA)on the basis of the long short term memory neural network autoregressive moving average model(LSTM-ARMA).Firstly,it decomposes the original InSAR time series into trend and noise components using wavelet denoising,employs the GRU recurrent neural network for rolling prediction of the trend component,and utilizes the ARMA model for forecasting the noise component.Subsequently,the sum of the predicted values of the trend and noise components is used as the total time series prediction value,thereby enhancing the prediction accuracy at each monitoring point.Finally,this paper selects multiple points(CP0001,CP0007,and CP0009)in the most severe subsidence area of the Argan mining area from 2020 to 2023 as examples for study.It demonstrates that the prediction accuracy of the wavelet-optimized combination model surpasses that of the traditional single models GRU/LSTM.Furthermore,compared to the LSTM-ARMA model,the predictive performance of the wavelet-optimized GRU-ARMA model is more stable,indicating it as an effective approach and method for surface subsidence prediction.
SBAS-InSARwavelet-optimized GRU-ARM A modelsurface subsidence predictionpoint-by-point predictionarea-wide prediction
万方数据
维普
