Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China
Wuhan, one of China's megacities with rapid development, is facing serious surface subsidence. In this study, we combined MT-InSAR, geo-detector, and LSTM (Long Short-Term Memory) to achieve the monitoring, analysis, and prediction of surface subsidence in the main urban districts of Wuhan. The...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-10-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S030324342100129X |
| _version_ | 1811328820444135424 |
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| author | Qing Ding Zhenfeng Shao Xiao Huang Orhan Altan Qingwei Zhuang Bin Hu |
| author_facet | Qing Ding Zhenfeng Shao Xiao Huang Orhan Altan Qingwei Zhuang Bin Hu |
| author_sort | Qing Ding |
| collection | DOAJ |
| description | Wuhan, one of China's megacities with rapid development, is facing serious surface subsidence. In this study, we combined MT-InSAR, geo-detector, and LSTM (Long Short-Term Memory) to achieve the monitoring, analysis, and prediction of surface subsidence in the main urban districts of Wuhan. The effectiveness of MT-InSAR in monitoring surface subsidence was validated against leveling results. During the monitoring period, the maximum subsidence velocity and uplift velocity were −53.3 mm/year and 18.0 mm/year, respectively. We identified six subsidence regions and explored their deformation characteristics. Further, we analyzed the relationship between the surface subsidence and influencing factors using the geo-detector in a quantitative manner. Our study revealed that the distance to soft soils had the greatest explanatory power on the subsidence. However, we also confirmed that subsidence was affected via coupling effects from multiple factors, suggesting a complex reinforcing relationship among influencing factors. The interaction between the distance to soft soils and the distance to karst collapse prone areas had the largest joint explanatory power on subsidence. Further, we constructed a data-driven LSTM model to predict and analyze the subsidence. The results showed that the LSTM model achieved great performance and presented strong universality, suggesting that it can be used for subsidence prediction in large geographic areas. |
| first_indexed | 2024-04-13T15:32:50Z |
| format | Article |
| id | doaj.art-17e99d568ee04284995633a86f9744c0 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T15:32:50Z |
| publishDate | 2021-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-17e99d568ee04284995633a86f9744c02022-12-22T02:41:20ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-10-01102102422Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, ChinaQing Ding0Zhenfeng Shao1Xiao Huang2Orhan Altan3Qingwei Zhuang4Bin Hu5State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; Corresponding author at: No. 129 Luoyu Road, Hongshan District, Wuhan 430079, Hubei, China.Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USADepartment of Geomatics Engineering, Istanbul Technical University, Istanbul 36626, TurkeyState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaWuhan, one of China's megacities with rapid development, is facing serious surface subsidence. In this study, we combined MT-InSAR, geo-detector, and LSTM (Long Short-Term Memory) to achieve the monitoring, analysis, and prediction of surface subsidence in the main urban districts of Wuhan. The effectiveness of MT-InSAR in monitoring surface subsidence was validated against leveling results. During the monitoring period, the maximum subsidence velocity and uplift velocity were −53.3 mm/year and 18.0 mm/year, respectively. We identified six subsidence regions and explored their deformation characteristics. Further, we analyzed the relationship between the surface subsidence and influencing factors using the geo-detector in a quantitative manner. Our study revealed that the distance to soft soils had the greatest explanatory power on the subsidence. However, we also confirmed that subsidence was affected via coupling effects from multiple factors, suggesting a complex reinforcing relationship among influencing factors. The interaction between the distance to soft soils and the distance to karst collapse prone areas had the largest joint explanatory power on subsidence. Further, we constructed a data-driven LSTM model to predict and analyze the subsidence. The results showed that the LSTM model achieved great performance and presented strong universality, suggesting that it can be used for subsidence prediction in large geographic areas.http://www.sciencedirect.com/science/article/pii/S030324342100129XUrban surface subsidenceSynthetic aperture radar interferometryGeo-detectorLong short-term memory network |
| spellingShingle | Qing Ding Zhenfeng Shao Xiao Huang Orhan Altan Qingwei Zhuang Bin Hu Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China International Journal of Applied Earth Observations and Geoinformation Urban surface subsidence Synthetic aperture radar interferometry Geo-detector Long short-term memory network |
| title | Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China |
| title_full | Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China |
| title_fullStr | Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China |
| title_full_unstemmed | Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China |
| title_short | Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China |
| title_sort | monitoring analyzing and predicting urban surface subsidence a case study of wuhan city china |
| topic | Urban surface subsidence Synthetic aperture radar interferometry Geo-detector Long short-term memory network |
| url | http://www.sciencedirect.com/science/article/pii/S030324342100129X |
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