{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:30:57Z","timestamp":1783009857281,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013200 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.<\/jats:p>","DOI":"10.3390\/rs12244044","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T08:59:34Z","timestamp":1607590774000},"page":"4044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method"],"prefix":"10.3390","volume":"12","author":[{"given":"Liyuan","family":"Shi","sequence":"first","affiliation":[{"name":"Key Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100048, China"},{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huili","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100048, China"},{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100048, China"},{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaofan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of the Ministry of Education Land Subsidence Mechanism and Prevention, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Observation and Research Station of Groundwater and Land Subsidence in Beijing-Tianjin-Hebei Plain, MNR, Beijing 100048, China"},{"name":"Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s10040-016-1386-y","article-title":"Preface: Land subsidence processes","volume":"24","author":"Galloway","year":"2016","journal-title":"Hydrogeol. J."},{"key":"ref_2","first-page":"1940","article-title":"Land subsidence in Mexico City mapped by ERS differential SAR interferometry","volume":"4","author":"Strozzi","year":"1999","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"471","DOI":"10.5026\/jgeography.78.7_471","article-title":"Land Subsidence and its Problems: With reference to the results of International Symposium on Land Subsidence, Tokyo, 1969","volume":"78","author":"Yamamoto","year":"1970","journal-title":"J. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.enggeo.2004.06.006","article-title":"Review on current status and challenging issues of land subsidence in China","volume":"76","author":"Hu","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_5","first-page":"328","article-title":"Subsidence mapping at regional scale using persistent scatters interferometry (PSI): The case of Tuscany region (Italy)","volume":"52","author":"Rosi","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/978-94-015-8719-8_6","article-title":"Land Subsidence in Bangkok during 1978\u20131988","volume":"2","author":"Nutalaya","year":"1996","journal-title":"Sea-Level Rise Coast. Subsid."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.quascirev.2010.11.017","article-title":"Coastal subsidence in Oregon, USA, during the giant Cascadia earthquake of AD 1700","volume":"30","author":"Hawkes","year":"2011","journal-title":"Quat. Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"365","DOI":"10.3390\/rs10030365","article-title":"Regional Land Subsidence Analysis in Eastern Beijing Plain by InSAR Time Series and Wavelet Transforms","volume":"10","author":"Mingliang","year":"2018","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zuo, J., Gong, H., Chen, B., Liu, K., Zhou, C., and Ke, Y. (2019). Time-series evolution patterns of land subsidence in the Eastern Beijing Plain, China. Remote Sens., 11.","DOI":"10.3390\/rs11050539"},{"key":"ref_10","first-page":"2","article-title":"Analysis and Outlook of the Land Subsidence in Beijing","volume":"7","author":"Yan","year":"2012","journal-title":"Urban Geol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, C., Gong, H., Chen, B., Gao, M., and Shi, M. (2020). Land Subsidence Response to Different Land Use Types and Water Resource Utilization in Beijing-Tianjin-Hebei, China. Remote Sens., 12.","DOI":"10.3390\/rs12030457"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1007\/s11069-012-0220-7","article-title":"Analysis of urbanisation-induced land subsidence in Shanghai","volume":"63","author":"Xu","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1680\/geot.2004.54.2.143","article-title":"Land subsidence due to groundwater drawdown in Shanghai","volume":"56","author":"Chai","year":"2004","journal-title":"Geotechnique"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yan, X.X., Gong, S.L., and Zeng, Z. (2002). Relationship between building density and land subsidence in Shanghai urban zone. Hydrogeol. Eng. Geol.","DOI":"10.1023\/A:1021249521259"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, Q., Ke, Y., Zhang, D., Chen, B., Gong, H., Lv, M., Zhu, L., and Li, X. (2018). Multi-Scale Analysis of the Relationship between Land Subsidence and Buildings: A Case Study in an Eastern Beijing Urban Area Using the PS-InSAR Technique. Remote Sens., 10.","DOI":"10.3390\/rs10071006"},{"key":"ref_16","first-page":"4","article-title":"Synthetic aperture radar interferometry","volume":"14","author":"Bamler","year":"1999","journal-title":"Inverse Probl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/5.838084","article-title":"Synthetic aperture radar interferometry","volume":"88","author":"Rosen","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hanssen, R.F. (2001). Radar Interferometry Data Interpretation and Error Analysis, Springer Science and Business Media.","DOI":"10.1007\/0-306-47633-9"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1130\/0091-7613(1999)027<0483:STUADO>2.3.CO;2","article-title":"Sensing the ups and downs of Las Vegas: InSAR reveals structural control of land subsidence and aquifer-system deformation","volume":"27","author":"Amelung","year":"1999","journal-title":"Geology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2013.08.038","article-title":"Land subsidence in central Mexico detected by ALOS InSAR time-series","volume":"140","author":"Chaussard","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1038\/35090558","article-title":"Tectonic contraction across Los Angeles after removal of groundwater pumping effects","volume":"412","author":"Bawden","year":"2001","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent scatterers in SAR interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"221","article-title":"Resolving vertical tectonics in the San Francisco Bay Area from permanent scatterer InSAR and GPS analysis","volume":"34","author":"Hilley","year":"2006","journal-title":"Geology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2014.08.004","article-title":"Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSAR interferometry","volume":"154","author":"Ge","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_25","first-page":"B07407","article-title":"Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volc\u00e1n Alcedo, Gal\u00e1pagos","volume":"112","author":"Hooper","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"139111","DOI":"10.1016\/j.scitotenv.2020.139111","article-title":"Land subsidence and its relation with groundwater aquifers in Beijing Plain of China","volume":"735","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_27","first-page":"1261","article-title":"Land subsidence research in Beijing based on the permanent Scatterers InSAR technology","volume":"19","author":"Gong","year":"2009","journal-title":"China Acad. J. Electron. Publ. House"},{"key":"ref_28","first-page":"2598","article-title":"Extracting 3D ground deformation velocity field by multi-platform persistent scatterer SAR interferometry Chinese","volume":"55","author":"Liu","year":"2012","journal-title":"J. Geophys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.econlet.2017.06.032","article-title":"Developing news-based Economic Policy Uncertainty index with unsupervised machine learning","volume":"158","year":"2017","journal-title":"Econ. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0167-2789(86)90240-X","article-title":"The immune system, adaptation, and machine learning","volume":"22","author":"Farmer","year":"1986","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.proeps.2011.09.029","article-title":"Study on Artificial Neural Network Method for Ground Subsidence Prediction of Metal Mine","volume":"2","author":"Zhao","year":"2011","journal-title":"Procedia Earth Planet. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zamanirad, M., Sarraf, A., Sedghi, H., Saremi, A., and Rezaee, P. (2019). Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms. Nat. Resour. Res., 1\u201315.","DOI":"10.1007\/s11053-019-09490-9"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.jenvman.2019.02.020","article-title":"Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities","volume":"236","author":"Rahmati","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.scitotenv.2019.03.496","article-title":"Land subsidence modelling using tree-based machine learning algorithms","volume":"672","author":"Rahmati","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tsangaratos, P., Ilia, I., and Loupasakis, C. (2019). Land Subsidence Modelling Using Data Mining Techniques. The Case Study of Western Thessaly, Greece. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques, Springer.","DOI":"10.1007\/978-3-319-73383-8_4"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.geomorph.2019.03.017","article-title":"Quantifying the contribution of multiple factors to land subsidence in the Beijing Plain, China with machine learning technology","volume":"335","author":"Zhou","year":"2019","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2012). Ensemble Methods: Foundations and Algorithms, Taylor & Francis, CRC Press.","DOI":"10.1201\/b12207"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.neucom.2014.07.064","article-title":"Neighbourhood sampling in bagging for imbalanced data","volume":"150","author":"Blaszczynski","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2016.04.001","article-title":"Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction","volume":"58","author":"Ziba","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tang, B., Chen, Q., Wang, X., and Wang, X. (2010). Reranking for Stacking Ensemble Learning. Neural Inf. Process. Theory Algorithm.","DOI":"10.1007\/978-3-642-17537-4_70"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Son, J., Jung, I., Park, K., and Han, B. (2015, January 7\u201313). Tracking-by-Segmentation with Online Gradient Boosting Decision Tree. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.350"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the KDD\u201916: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bi, Y., Xiang, D., Ge, Z., Li, F., Jia, C., and Song, J. (2020). An Interpretable Prediction Model for Identifying N 7-Methylguanosine Sites Based on XGBoost and SHAP. Mol. Ther. Nucleic Acids.","DOI":"10.1016\/j.omtn.2020.08.022"},{"key":"ref_45","first-page":"11","article-title":"Box-Office Revenue Predictions Based on XGBoost and Sentiment Analysis","volume":"6","author":"Xu","year":"2020","journal-title":"World Sci. Res. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"118848","DOI":"10.1016\/j.fuel.2020.118848","article-title":"A New Method of Diesel Fuel Brands Identification: SMOTE Oversampling Combined with XGBoost Ensemble Learning","volume":"282","author":"Wang","year":"2020","journal-title":"Fuel"},{"key":"ref_47","first-page":"4","article-title":"Preliminary Study on Selection Schemes of Groundwater Environmental Background Values in Beijing Area","volume":"14","author":"Yuanzhang","year":"2019","journal-title":"Urban Geol."},{"key":"ref_48","first-page":"1","article-title":"Division of Water-bearing Zones and Compressible Layers in Beijing\u2019s Land Subsidence Areas","volume":"2","author":"Yu","year":"2007","journal-title":"City Geol."},{"key":"ref_49","first-page":"54","article-title":"Land subsidence lagging quantification in the main exploration aquifer layers in Beijing plain, China","volume":"75","author":"Chen","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","first-page":"8","article-title":"Progress of Permanent Scatterer Interferometry","volume":"29","author":"Deren","year":"2004","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2004GL021737","article-title":"A new method for measuring deformation on Volcanoes and other natural terrains using InSAR Persistent Scatterers","volume":"31","author":"Hooper","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.1080\/01431160802039957","article-title":"A new index for delineating built-up land features in satellite imagery","volume":"29","author":"Xu","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Enviorn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1214\/aos\/1176347502","article-title":"Estimation in a Linear Regression Model with Censored Data","volume":"18","author":"Ritov","year":"1990","journal-title":"Ann. Stat."},{"key":"ref_57","first-page":"513","article-title":"Principal Component Analysis","volume":"87","author":"Jolliffe","year":"2002","journal-title":"J. Mark. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCSE.2007.58","article-title":"Python for Scientific Computing","volume":"9","author":"Oliphant","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Aiken, L.S., West, S.G., and Pitts, S.C. (2003). Multiple Linear Regression. Handbook of Psychology, John Wiley & Sons, Inc.","DOI":"10.1002\/0471264385.wei0219"},{"key":"ref_60","unstructured":"Chen, B. (1986). Polynomial Regression. Springer Texts Stats, 235\u2013268."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Breiman, L. (1996). Bagging predictors. Mach. Learn.","DOI":"10.1007\/BF00058655"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2307\/2346413","article-title":"Fitting Segmented Regression Models by Grid Search","volume":"29","author":"Lerman","year":"1980","journal-title":"Appl. Stats."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4044\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:43:22Z","timestamp":1760179402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":62,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12244044"],"URL":"https:\/\/doi.org\/10.3390\/rs12244044","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,10]]}}}