In this study, we propose a model to accurately predict the ground subsidence caused by subway excavation using the wavelet denoising model and BP neural network. First, we develop an optimal denoising model by comparing and analyzing the denoising effect of different wavelet denoising parameters. The model is used to reduce the noise of the monitoring data. Then, we utilize BP neural network to develop a prediction model in which the proposed denoising model is used. Finally, we apply the proposed model to Urumqi subway. The results demonstrate the rationality and accuracy of the proposed model.

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