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Multiobjective Analysis of a Public Wellfield Using Artificial Neural Networks
Author(s) -
Coppola Emery A.,
Szidarovszky Ferenc,
Davis Donald,
Spayd Steven,
Poulton Mary M.,
Roman Eric
Publication year - 2006
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2006.00248.x
Subject(s) - water supply , stakeholder , vulnerability (computing) , water balance , aquifer , groundwater recharge , groundwater , operations research , environmental science , computer science , environmental economics , engineering , environmental engineering , economics , computer security , management , geotechnical engineering
As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. 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 ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN‐derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade‐off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. 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.