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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. Full article

The Chinese government hopes to implement groundwater table control to realize the sustainable utilization of groundwater resources based on controlling the current groundwater exploitation amount. In this study, a method to determine the control index of the groundwater table is proposed. In the method, the reasonable relationship between the groundwater table and groundwater exploitation amount is ensured using the groundwater numerical simulation model. The operability of the index determination is improved using a surrogate numerical model, and the annual hydrological dynamic is simplified to three scenarios of dry, flat, and wet. To verify this method, the Minqin Basin in Northwest China was chosen as a typical study area. It is assumed that the control index of groundwater exploitation in 2020 is 85,000 × 10³m³. Then, the preset annual water table index is calculated as [−0.70, 0.62, 1.13, −1.25, 1.36, 3.09] m [−0.77, 0.53, 1.05, −1.33, 1.27, 2.96] m, and [−0.83, 0.46, 0.99, −1.40, 1.20, 2.85] m for the chosen six monitoring wells, varying over the years with wet, flat, and dry scenarios. This method can ensure high precision, operability, and dynamic management when determining the control index of the groundwater table and satisfy the demand of managers. Full article