A multi-objective optimization approach to groundwater management using genetic algorithm

Abstract

Management of groundwater resources is very important for regions where freshwater supply is naturally limited. Long-term planning of groundwater usage requires method-based new decision support tools. These tools must be able to predict the change in the groundwater storage with sufficient accuracy, and must allow exploring management scenarios with respect to different criteria such as sustainability and cost. So, a multi-objective optimization algorithm is used for groundwater management problem. In this paper, a genetic algorithm with two additional techniques, Pareto optimality ranking and fitness sharing, is applied to simultaneously maximize the pumping rate and minimize pumping cost. The methodology proposed has more Pareto optimal solutions. However, it is desirable to get, and to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to help the decision maker. A groundwater resources management model in which performed through a combined simulation-optimization model is used. This multi-objective genetic algorithm (MOGA) of optimization combines the modular three-dimensional finite-difference (MODFLOW) and genetic algorithm (GA). MOGA model is applied in El-Farafra oasis, Egypt to develop the maximum pumping rate and minimum operation cost as well as the prediction of the future changes in both pumping rate and pumping operation cost. It also makes a feasible solution in groundwater management. Finally, a compromise solution is presented from a set of Pareto optimal solutions.   Key words: Groundwater management, multi-objective optimization, genetic algorithm, Farafra oasis, Egypt.