Application of improved wavelet denoising algorithm and SSA prediction model in subway monitoring
In order to effectively remove noise from subway deformation monitoring signals and improve deformation monito-ring accuracy,this paper designs a wavelet denoising algorithm with a threshold of 1/2 noise amplitude to denoise real-time subway displacement monitoring signals.The research results show that compared with hard threshold method,soft threshold method,and adjustable parameter threshold function method,the 1/2 threshold function method has the best denoising effect,with an average SNR of 24.627dB.Compared with hard threshold method,soft threshold method,and adjustable pa-rameter threshold function method,it has increased by 3.15%,3.12%,and 1.07%,respectively.The average RMSE is 0.23mm,which is10.07%,6.47%,and4.32%higher than the hard threshold method,soft threshold method,and adjustable parameter threshold function method,respectively.The singular spectrum analysis(SSA)prediction model was used to pre-dict the monitoring data.The prediction accuracy was as follows:the average MAE was 0.24mm,and the RMSE value was 0.26mm.In terms of RMSE value,it improved by 16.45%compared to the BP neural network.