An analysis of different strategies for the prioritization of groundwater quality prediction studies with a sequential numerical game

Abstract

Groundwater quality prediction studies are carried out to increase the reaction time when drinking water companies have to respond to breakthroughs of contaminants. Drinking water companies exploit numerous wells and need to decide on research priorities for these wells, as budgets are limited. The reliability and accuracy of predictions improve if more funds are invested in data-collection and prediction studies, but there is no clear decision model available to determine the required level of (un)certainty. Hence, it is unclear which prioritization strategy is optimal. Unnecessary losses can occur if inappropriate strategies are followed. A decision analysis of strategies for prioritizing prediction studies is presented in this paper, where the problem is posed as an optimization problem with an explicit loss function. A sequential numerical game was set up in order to assess the effectiveness of different strategies. There were significant differences between the performances of strategies. The most successful strategy used the anticipated uncertainty reduction of additional studies as one of the prioritization criteria and takes the uncertainty of predictions in to account.