Open AccessPredicting the settlement of Urumqi subway based on wavelet denoising and BP neural network
Author(s) -
J Q Zhang,
Yuanyuan Qin,
Lei Xie
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/570/5/052019
Subject(s) - noise reduction , artificial neural network , wavelet , computer science , artificial intelligence , ground subsidence , noise (video) , pattern recognition (psychology) , data mining , engineering , image (mathematics) , mining engineering
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.