Emery A. Coppola Jr,Ferenc Szidarovszky,Steven Spayd,Mary M. Poulton,Eric Roman, NOAH LLC, , ecoppolajr@gmail.com
Abstract
As competition for water resources grows, multiobjective optimization may help identify appropriate management policies, particularly when disparate stakeholders are involved with conflicting objectives. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby contaminant plume. With few alternatives, decision makers must balance these two conflicting objectives. Using a transient simulation model for the wellfield consisting of artificial neural networks, a formal multiobjective optimization model was developed, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was determined. Relative preference values and power factors were assigned to the three stakeholders, A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. The above methodology can have many other applications to real-time adaptive water resources management.
Keywords: ground water management, artificial neural networks, multiobjective optimization