Optimization of a Basin Network Using Hybridized Global Search Algorithms

Abstract

Over the last few decades, groundwater resources in many regions have been depleted at a higher rate than the underlying aquifers have been replenished. This imbalance has led water management agencies to consider managed aquifer recharge networks, in which infiltration basins are used to replenish the aquifers using previously uncaptured stormwater runoff. In this work, optimization methods were used to select parameter values to minimize the cost associated with constructing such a network while ensuring the network has the ability to supplement demands placed on the aquifer. The objective function considered incorporates land and construction costs, along with rewards for effective aquifer recharge, and constraints were incorporated to enforce capture of a minimum volume of stormwater runoff. Two hybridized global search algorithms were considered, one based on particle swarm optimization and the other on a genetic algorithm approach. Both methods returned solutions that were close in terms of minimal cost but varied in terms of individual basin sizes. Thus, the algorithms are able to aid decision makers by providing several cost-competitive solutions that can then be used to support a community dialogue.