In order to evaluate the groundwater resources and aquifer system of the Jilin urban area (JUA), a groundwater numerical flow model was established by using the groundwater modeling system based on data from 190 boreholes. River stages were interpolated to control the groundwater flow field. The input parameters such as hydraulic conductivity and specific yield were based on data from 260 pumping test data. The model was calibrated by trial and error, simulated results were compared to the observed head and contour maps, which were generally in good agreement, and the root mean squared error was 0.66 m. Sensitivity analysis was carried out and recharge proved to be the most sensitive factor in this model. The water budget showed that the input was 2.07× 10⁸ m³/a, which was smaller than the output of 2.21 × 10⁸ m³/a. A groundwater level decline and cone of depression exist in the Songhua and Aolong river valley. The JUA aquifer systems can be well and efficiently modeled by constructing a numerical model. Based on the supply and demand analysis of water resources, the established model would finally provide a scientific basis to use the groundwater resources sustainably in JUA.

Seawater intrusion is expected to cause a shortage of freshwater resources in coastal areas which will hinder regional economic and social development. The consequences of global climate change include rising sea levels, which also affect the results of the predictions of seawater intrusion that are based on simulations. It is thus important to examine the impact of the randomness in the rise in sea levels on the uncertainty in the results of numerical simulations that are used to predict seawater intrusion. Deep learning has lately emerged as a popular area of research that has been used to establish surrogate models in this context. In this study, the authors have used deep learning to determine the complex and nonlinear mapping relationship between the inputs and outputs of a three-dimensional variable-density numerical model of seawater intrusion in the case of a limited number of training samples, wherein, this has improved the accuracy of the approximation of the surrogate models. We used the rise in sea level as a random variable, and then applied the Monte Carlo method to analyze the influence of randomness on the uncertainty in the results of the numerical predictions of seawater intrusion. Statistical analyses and interval estimations of the Cl⁻ concentration and the area of seawater intrusion were conducted at typical observation wells. The work that is here provides a reliable reference for decision making in the area.