Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method
In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land su...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/24/4044 |
| _version_ | 1797545093021827072 |
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| author | Liyuan Shi Huili Gong Beibei Chen Chaofan Zhou |
| author_facet | Liyuan Shi Huili Gong Beibei Chen Chaofan Zhou |
| author_sort | Liyuan Shi |
| collection | DOAJ |
| description | In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150–200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By using XGBoost to predict the subsidence amount, our research provides a new idea for land subsidence prediction. |
| first_indexed | 2024-03-10T14:10:36Z |
| format | Article |
| id | doaj.art-443541c1008543c1a8e1e27048cd5f52 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:10:36Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-443541c1008543c1a8e1e27048cd5f522023-11-21T00:12:30ZengMDPI AGRemote Sensing2072-42922020-12-011224404410.3390/rs12244044Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning MethodLiyuan Shi0Huili Gong1Beibei Chen2Chaofan Zhou3Key Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, ChinaKey Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, ChinaKey Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, ChinaKey Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, ChinaIn the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150–200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By using XGBoost to predict the subsidence amount, our research provides a new idea for land subsidence prediction.https://www.mdpi.com/2072-4292/12/24/4044land subsidencepersistent scatters interferometryremote sensingmachine learning |
| spellingShingle | Liyuan Shi Huili Gong Beibei Chen Chaofan Zhou Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method Remote Sensing land subsidence persistent scatters interferometry remote sensing machine learning |
| title | Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method |
| title_full | Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method |
| title_fullStr | Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method |
| title_full_unstemmed | Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method |
| title_short | Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method |
| title_sort | land subsidence prediction induced by multiple factors using machine learning method |
| topic | land subsidence persistent scatters interferometry remote sensing machine learning |
| url | https://www.mdpi.com/2072-4292/12/24/4044 |
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