Study on land subsidence prediction along the subway lines in Shanghai city
In view of the problem of prevention and control of land subsidence along subway lines,a rolling prediction method based on the time series formed with monthly cumulative settlement was established in this paper,and a combination prediction method named as"linear regression+BP neural network"was proposed.According to the interferometric synthetic aperture radar(InSAR)monitoring results of land subsidence along subway lines from 2013 to 2020,three persistent scatter(PS)points representing different characteristics of land subsidence evolution were used to evaluate the predictive effects of three models,named respectively as multiple linear regression,BP neural network and"linear regression+BP neural network".The results showed that the prediction accuracy of"linear regression+BP neural network"was the highest in the three cases,which reflected the availability and superiority of the combined prediction method,and had reference value for improving the accuracy of land subsidence prediction.
NETL
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万方数据
