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. As a result, a groundwater exploitation scheme was obtained for a typical study area, which provided a scientific basis and a reference for the rational development of effective groundwater resource solutions. The results of this study can be described as follows. (1) By introducing the theory and method of deep learning from artificial intelligence, the problem of complex nonlinear mapping between the inputs and outputs of a three-dimensional variable-density seawater intrusion numerical simulation model under the condition of limited number of training samples is effectively solved, and the approximation accuracy of the surrogate model with respect to the simulation model is improved. (2) By solving the optimization model, a reasonable groundwater exploitation scheme was obtained, which provided a scientific basis for the rational development and efficient use of groundwater resources in the study area.
Keywords: Artificial intelligence; Deep belief neural network; Sea-level rise; Seawater intrusion; Surrogate model.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.