Pumping Optimization of Coastal Aquifers Using Seawater Intrusion Models of Variable-Fidelity and Evolutionary Algorithms

Variable-fidelity modelling has been utilized in several engineering optimization studies to construct surrogate models. However, similar approaches have received much less attention in coastal aquifer management problems. A variable-fidelity optimization framework was developed utilizing a lower-fidelity and computationally cheap model of seawater intrusion, based on the sharp interface assumption, and a simple correction process. The variable-fidelity method was compared to the direct optimization with the high-fidelity variable density and salt transport model and to conventional surrogate-based optimization. The surrogate-based approaches were embedded into the operations of an evolutionary algorithm to implement an efficient online update of the surrogate models and control the feasibility of the optimal solutions. Multiple independent optimization runs were performed to provide more insightful comparison outcomes. Although the variable-fidelity method found a better optimum than the conventional approach, the overall sample statistics showed that the surrogate-based optimization frameworks performed equally well and provided good approximations to the high-fidelity solution. Despite the potential for an improved exploration of the optimal search space by using the variable-fidelity method, the conventional approach had a 30% faster average convergence time.