Ground surface monitoring (GSM) points collect information for mining surface subsidence monitoring and environmental governance. However, GSM points submerge in high groundwater mining areas, preventing the collection of monitoring data. The application of machine learning (ML) algorithms to subsidence prediction ignores the uncertainty and irregularity in subsidence changes. Thus, an innovative GSM point information prediction model, which improves the multikernel support vector machine (GA-MK-SVM) using chaos residual theory commonly used for capturing GSM point information, is proposed. The mean relative errors (MREs) between the predicted and observed results of GA-SVM and GA-MK-SVM were 8.2% and 6.1% during active periods, respectively. The GA-MK-SVM also performed better than the GA-SVM during stable periods. The residual error accumulates as the ML algorithms progress, resulting in imprecise predictions of the GSM points. Thus, the GA-MK-SVM model was improved using chaotic theory (Chaos-GA-MK-SVM), with MREs of 5.0% and 0.9% during the active and stable periods, respectively. The accuracy of the proposed model was improved by 1.1% and 3.2% compared with the unimproved GA-MK-SVM, respectively. The proposed approach provides practical GSM point information for mining subsidence studies and governance in high groundwater mines.

地表监测 (GSM) 点收集用于采矿地表沉降监测和环境治理的信息。然而，GSM 点淹没在高地下水矿区，妨碍了监测数据的收集。机器学习（ML）算法在沉降预测中的应用忽略了沉降变化的不确定性和不规则性。因此，提出了一种创新的GSM点信息预测模型，该模型利用通常用于捕获GSM点信息的混沌残差理论改进了多核支持向量机（GA-MK-SVM）。在活动期间，GA-SVM 和 GA-MK-SVM 的预测结果和观察结果之间的平均相对误差 (MRE) 分别为 8.2% 和 6.1%。在稳定期间，GA-MK-SVM 的表现也优于 GA-SVM。残差随着机器学习算法的进展而累积，导致对 GSM 点的预测不准确。因此，使用混沌理论 (Chaos-GA-MK-SVM) 改进了 GA-MK-SVM 模型，在活跃期和稳定期的 MRE 分别为 5.0% 和 0.9%。与未改进的 GA-MK-SVM 相比，该模型的准确率分别提高了 1.1% 和 3.2%。所提出的方法为高地下水矿的采矿沉降研究和治理提供了实用的 GSM 点信息。与未改进的 GA-MK-SVM 相比，分别为 2%。所提出的方法为高地下水矿的采矿沉降研究和治理提供了实用的 GSM 点信息。与未改进的 GA-MK-SVM 相比，分别为 2%。所提出的方法为高地下水矿的采矿沉降研究和治理提供了实用的 GSM 点信息。