Optimal Groundwater Remediation Using Artificial Neural Networks

The significant cost and complexity of groundwater remediation and water resources management has encouraged integration of optimization techniques with groundwater flow and transport modeling to search for efficient groundwater management strategies. This integration of methodologies has often been referred to as simulationmanagement modeling or simulation-optimization groundwater management modeling. The values of the objective function and constraints of the optimization problem are calculated by the groundwater flow and transport models run as a submodel of the optimization driver. In the area of remediation, example objectives could be minimizing costs or maximizing contaminant mass removed; constraints might be avoiding dewatering or a total pumping volume limit. A general groundwater management model will use optimization techniques to search among almost infinite numbers of treatment or control strategies possibilities for ones that meet management goals while minimizing cost. The main advantage of applying these mathematical tools to decision-making problems is that they are less restricted by human imagination than case-by-case comparisons. As the number of competing engineering, economic, and environmental planning objectives and constraints increases, it becomes difficult for human planners to track complex interactions and select a manageable set of promising scenarios for examination. Using optimization techniques, the search can range over all possible combinations of variables, locating strategies whose effectiveness is not always obvious to planners.