Optimal Management of Groundwater Extraction to Control Saltwater Intrusion in Multi-Layered Coastal Aquifers Using Ensembles of Adaptive Neuro-Fuzzy Inference System

Abstract

Regional scale management of coastal aquifers for control of saltwater intrusion is a challenging problem, requiring solution of optimization and simulation models. Simulation of density dependent nonlinear flow and transport processes in a coastal aquifer requires the solution of coupled flow and transport equations. Prescription of optimal spatial and temporal management strategy for coastal aquifers is possible by utilizing a linked simulation-optimization approach. However, such linked models require the iterative and numerical simulation of the flow and transport processes numerous number of times within an optimization algorithm. In order to ensure computational feasibility and efficiency, trained and tested surrogate models with acceptable accuracy and efficiency can be used as approximate simulators within an optimization algorithm. In this study, an efficient surrogate model based on ensemble of Adaptive Neuro-fuzzy Inference System (ANFIS) is developed and evaluated as an approximate simulator of the physical processes of a multi-layered coastal aquifer. The management of coastal aquifers is also multiple-objective in nature. Therefore, the developed surrogate model is linked to a Controlled Elitist Multi-objective Genetic Algorithm (CEMGA). Ensembles of the surrogate models (En-ANFIS) are utilized in order to incorporate uncertainties in prediction using surrogate models. The proposed simulation-optimization framework is implemented in a parallel computing platform to achieve further computational efficiency. The performance of the multi-objective management model is evaluated for an illustrative study area. The evaluation results indicate that ANFIS based ensemble-modelling approach together with CEMGA is able to evolve reliable strategies for this multiple objective management of coastal aquifers.