A surrogate based multi-objective management model to control saltwater intrusion in multi-layered coastal aquifer systems

ABSTRACT Linked simulation-optimisation (S–O) models need to simulate the physical processes either by using a rigorous numerical model, or a trained surrogate model approximating the physical processes. A methodology is proposed to evolve Pareto optimal management strategies for a multi-layered coastal aquifer system using a trained and tested Multivariate Adaptive Regression Spline (MARS) surrogate model linked to a multi-objective saltwater intrusion management model. Performance of the developed methodology is evaluated using an illustrative multi-layered coastal aquifer system. Solution results indicate that MARS is capable of approximately replacing the more rigorous numerical simulation model within the linked S–O model to ensure computational efficiency and feasibility in applying such linked S–O models for coastal aquifer management problems. Furthermore, the ability of MARS to recognise the most relevant input variables in predicting the responses as outputs enables the construction of an efficient and robust surrogate model. Integration of parallel processing capabilities within the optimisation model is shown to improve computational efficiency and feasibly of solving such large scale multi-objective problems. Therefore, the developed methodology utilising the MARS based surrogate model is potentially applicable for developing optimal groundwater extraction strategies for sustainable regional scale management of coastal aquifers.