Application of artificial intelligence deep learning in numerical simulation of seawater intrusion.

Seawater intrusion not only causes fresh water shortages in coastal areas, but also has a negative impact on regional economic and social development. Global climate change will affect precipitation, sea level, and many other factors, which will in turn affect the simulation and prediction results for seawater intrusion. By combining groundwater numerical simulation technology, an atmospheric circulation model, artificial intelligence methods, and simulation optimization methods, this study coupled a numerical simulation model of seawater intrusion with an optimization model to optimize the groundwater exploitation scheme in the study area under the condition of climate change.

Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty.

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation.

Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models.

Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed.

Modeling and uncertainty analysis of seawater intrusion based on surrogate models.

When using a simulation model to study seawater intrusion (SI), uncertainty in the parameters directly affects the results. The impact of the rise in sea levels due to global warming on SI cannot be ignored. In this paper, the Monte Carlo method is used to analyze the uncertainty in modeling SI.

The application of 0-1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source.

The optimization model is presently used for the identification of pollution sources and it is based on non-linear programming optimization. The decision variables in this model are continuous, resulting in a weak recognition of integer variables including pollution source location. In addition, as the number of pollution sources increase, so the calculated load increases exponentially and accuracy decreases.

Application of ensemble surrogates and adaptive sequential sampling to optimal groundwater remediation design at DNAPLs-contaminated sites.

In this study, we aimed to develop an optimal groundwater remediation design for sites contaminated by dense non-aqueous phase liquids by using an ensemble of surrogates and adaptive sequential sampling. Compared with previous approaches, our proposed method has the following advantages: (1) a surrogate surfactant-enhanced aquifer remediation simulation model is constructed using a Gaussian process; (2) the accuracy of the surrogate model is improved by constructing ensemble surrogates using five different surrogate modelling techniques, i.e.