Optimal Management of Coastal Aquifers Using Linked Simulation Optimization Approach
Saltwater intrusion management models can be used to derive optimal and efficient management strategies for controlling saltwater intrusion in coastal aquifers. To obtain physically meaningful optimal management strategies, the physical processes involved need to be simulated while deriving the management strategies. The flow and transport processes involved in coastal aquifers are difficult to simulate especially when the density-dependent flow and transport processes need to be modeled. Incorporation of this simulation model within an optimization-based management model is very complex and difficult. However, as an alternative, it is possible to link a simulation model externally with an optimization-based management model. The GA-based optimization approach is especially suitable for externally linking the numerical simulation model within the optimization model. Further efficiency in computational procedure can be achieved for such a linked model, if the simulation process can be simplified by approximation, as very large number of iterations between the optimization and simulation model is generally necessary to evolve an optimal management strategy. A possible approach for approximating the simulation model is to use a trained Artificial Neural Network (ANN) as the approximate simulator. Therefore, an ANN model is trained as an approximator of the three dimensional density-dependent flow and transport processes in a coastal aquifer. A linked simulation – optimization model is then developed to link the trained ANN with the GA-based optimization model for solving saltwater management problems. The performance of the developed optimization model is evaluated using an illustrative study area. The evaluation results show the potential applicability of the developed methodology using a GA- and ANN-based linked optimization – simulation model for optimal management of coastal aquifer. Copyright Springer Science + Business Media, Inc. 2005
