Multi-objective optimization for optimal extraction of groundwater from a nitrate-contaminated aquifer considering economic-environmental issues: A case study

Abstract

This paper focuses on the multi-objective optimization of the groundwater extraction scheme in the Bouein-Myandasht aquifer (Iran) in order to reduce the concentration of nitrate, originating from agricultural activities and wastewater absorbent wells. A simulation-optimization model coupling an artificial neural network (ANN) as the simulator with the non-dominated sorting genetic algorithm-type II (NSGA-II) as the optimizer, are employed. The simulator is trained by help of data generated by process-based simulation models for groundwater flow (MODFLOW) and solute transport (MT3D). The optimization objectives include (1) minimizing the contamination concentration and (2) maximizing the net benefit of the agricultural activities. The outcome of the simulation-optimization model is an optimized management strategy formed by the optimal values of the optimization parameters searched and obtained consisting of (1) seasonal groundwater extraction volume; (2) the ratio of the wastewater which should be treated before being leached into the groundwater through the absorbent wells; (3) the ratio of the fertilizers consumption; and (4) the cultivated area for each of the main crops in the study area. The results of the model suggest a groundwater extraction policy fulfilling the objectives of the optimization. The optimal operating policy also indicates that a partly conflicting relation exists between minimizing the risk of groundwater contamination and maximizing the net benefits of the agricultural activities. Hence, the focus of this paper is at finding the better and better Pareto-fronts in the objective space while dealing with the parts of the objective functions with less conflict to reach the optimal Pareto-front on which the full conflict between the objectives is held. Then, an entropy-based trade-off reflected in designating a couple of weights assigned to the couple of objectives calculated for each solution in the bi-objective space is held over the solutions lying on the optimal Pareto-front and finally, the favorite solution minimizing the weighted-distance to the ideal point in the objective space is achieved using the TOPSIS method. With this policy the regional nitrate concentration will be decreased by 36.7%, 20.45% and 21.6% in the first, second and third study sub-areas, respectively, as compared to those in the actual operation. Furthermore, the model suggests 15%, 12% and 9% wastewater treatment and also 9%, 6% and 7% decrease in the fertilizer use in the first, second, and third study sub-areas, respectively.

Keywords: Artificial neural network (ANN); Crop pattern planning; Groundwater modeling; Multi-objective optimization; NSGA-II; Nitrate contamination.