Genetic algorithm tuned fuzzy inference system to evolve optimal groundwater extraction strategies to control saltwater intrusion in multi-layered coastal aquifers under parameter uncertainty

Abstract

Excessive withdrawal of groundwater resources poses significant challenges to the management of saltwater intrusion processes in coastal aquifers. Optimization of groundwater withdrawal rates plays a vital role in sustainable management of coastal aquifers. This study proposes a genetic algorithm (GA) tuned Fuzzy Inference System (FIS) hybrid model (GA-FIS) for developing a regional scale saltwater intrusion management strategy. GA is used to tune the FIS parameters in order to obtain the optimal FIS structure. The GA-FIS models thus obtained are linked externally to the Controlled Elitist Multi-objective Genetic Algorithm (CEMGA) in order to derive optimal pumping management strategies using a linked simulation–optimization approach. The performance of the hybrid GA-FIS-CEMGA based saltwater intrusion management model is compared with that of a basic adaptive neuro fuzzy inference system (ANFIS) based management model (ANFIS-CEMGA). The parameters of the ANFIS model are tuned using hybrid algorithm. To achieve computational efficiency, the proposed optimization routine is run in a parallel processing platform. An illustrative multi-layered coastal aquifer system is used to evaluate the performances of both management models. The illustrative aquifer system considers uncertainties associated with the hydrogeological parameters e.g. hydraulic conductivity, compressibility, bulk density, and aquifer recharge. The evaluation results show that the proposed saltwater intrusion management models are able to evolve reliable optimal groundwater extraction strategies to control saltwater intrusion for the illustrative multi-layered coastal aquifer system. However, a closer look at the performance evaluation results demonstrate the superiority of the GA-FIS-CEMGA based management model over ANFIS-CEMGA based saltwater intrusion management model.