Guided by the concepts of spatial control and target control in the Regulations on Groundwater Management, this study classifies the current research progress and status of groundwater overexploitation control and dual control of groundwater quantity and water level systems. Summary are that the research on groundwater overexploitation control system is increasingly closely integrated with China’s specific national conditions, but the relevant management systems and norms still need to be improved urgently. The groundwater overexploitation control system clearly defines the division standards for areas that have already experienced overexploitation, but lacks a unified and comprehensive groundwater resource control zoning standard. The dual control system of groundwater quantity and water level overcomes the limitations of unilateral control of water level or water quantity, failure to consider the inherent relationship between groundwater level and water quantity, and difficulty in effectively alleviating ecological and environmental problems caused by changes in water level or water quantity. The difficulty in the study of water volume and water level dual control system lies in the fact that the water level in the point distribution can be monitored to control the mining output of the areal distribution. It is suggested to control the groundwater exploitation in a certain period by managing the water level in a specific time, or control the point water level by controlling the areal mining output.

For the efficient identification of pollution events in water distribution networks using limited sensor monitoring data, we propose the MOEA-LRM algorithm as a method for optimizing the water quality sensor layout of water supply networks by integrating a multi-objective evolutionary algorithm (MOEA) with a logistic regression model (LRM). The effectiveness of this approach is demonstrated through its application to the Anytown and Fosspoly1 pipe network systems. The MOEA-LRM algorithm aims to minimize the number of sensors, as well as the average and worst-case impact risk, by constructing a mathematical model using the MOEA algorithm that achieves Pareto equilibrium within a pipe network system. Based on this premise, the MOEA-LRM algorithm leverages the LRM to efficiently screen and identify the optimal sensor layout, thereby enhancing the accuracy of contamination source identification across the entire network. The results illustrate that this approach consistently identifies an optimal sensor configuration that ensures accurate identification of the source of contamination throughout the pipe network and effectively reduces the impact of exogenous water pollution incidents on users.