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Global Geology 2022, 25(2) 109-115 DOI:
ISSN: 1673-9736 CN: 22-1371/P |
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摘要：
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Prediction of surface subsidence in Changchun City based on LSTM network |
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WANG He and WU Qiong |
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College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China |
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Abstract:
Monitoring and predicting of urban surface subsidence are important for urban disaster
prevention and mitigation. In this paper, the Long Short-Term Memory (LSTM) network was used
to predict the surface subsidence process of Changchun City from 2018 to 2020 based on PS-InSAR
monitoring data. The results show that the prediction error of 57.89% of PS points in the LSTM network was less than 1mm with the average error of 1.8 mm and the standard deviation of 2.8 mm. The accuracy and reliability of the prediction were better than regression analysis, time series analysis and grey model. |
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Keywords:
LSTM neural network
surface subsidence
PS-InSAR
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