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 (GA) with two additional techniques, Pareto optimality ranking and fitness sharing, was applied to simultaneously maximize the pumping rate and minimize pumping cost. The methodology proposed herein has more Pareto optimal solutions, however, it is desirable to find the ones scattered uniformly over the Pareto frontier to provide a variety of compromise solutions to the decision maker. A groundwater resources management model is performed through a combined simulation–optimization model. This model is called the multi-objective genetic algorithm (MOGA) for optimization which combines the MODFLOW and GA. MOGA model applied in Wadi El-Farigh, Egypt, to develop the maximum pumping rate and minimum operation cost as well as to predict the future changes in both pumping rate and pumping operation cost. Model makes a feasible solution in groundwater management. Finally a compromise solution is presented from a set of Pareto optimal solutions to help the decision maker.
Highlights
Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found
multi-objective genetic algorithm (MOGA) model applied in Wadi El-Farigh, Egypt, to develop the maximum pumping rate and minimum operation cost as well as to predict the future changes in both pumping rate and pumping operation cost
Since no one solution can be termed as an optimum solution to multiple conflicting objectives, the resulting multi-objective optimization problem resorts to a number of trade-off optimal solutions
Summary
Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. The presence of multiple conflicting objectives is natural in many groundwater management problems and makes the optimization problem interesting to solve. Since no one solution can be termed as an optimum solution to multiple conflicting objectives, the resulting multi-objective optimization problem resorts to a number of trade-off optimal solutions. Classical optimization methods can at best find one solution in one simulation run, thereby making those methods inconvenient to solve multi-objective optimization problems (Deb 2001). There usually exist a set of solutions for the multiple-objective case which cannot be compared with each other. For such solutions called Pareto optimal solutions, no improvement in any objective function is possible without sacrificing at least one of the other
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
