Abstract
Finding a strategy that allows economically efficient drinking water production in regional supply
systems at minimal environmental cost is often a complex task. In order to determine the optimal
spatial production configuration, a systematic trade off among costs and benefits of possible strategies
is required. Such a trade-off involves the handling of pronounced
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non-linear relations between
quantitative aspects of strategies and their corresponding impacts. We developed a computer-based
methodology for multiple objective optimisation of drinking water production by combining 'Min Cost
Flow' and Genetic Algorithms (GA). The impact of production strategies is assessed by environmental,
economic and geo-hydrologic modelling. Finding the optimal solution requires valuation of objective
categories by translating impacts into a common scale and/or by definition of constraints that are
specific for a particular category. If the impact of a category cannot be converted a priori to a common
scale, a Pareto frontier of non-inferior solutions is calculated. Thus, the interdependency of impact
categories can be clarified and decision makers and stakeholders are facilitated in the selection of
appropriate production strategies. The approach was implemented in a GIS-based decision support
system in order handle all spatial relations efficiently and to offer decision makers an adequate access
to the methodology.
Groundwater quality prediction studies are frequently carried out within the framework of drinking
water supply in order to assess the future composition of groundwater that will be pumped at
production wells. These prediction studies help to assure a safe supply of drinking water in the future.
Regional drinking water companies typically exploit numerous pumping wells and need to decide on
research priorities for these wells as budgets are limited. Assessment of the uncertainty of prediction
studies has been a scientific topic for many years, particularly when numerical models are used as
predictive tools. Sophisticated techniques for the quantification of the uncertainty of model results have
been developed over the past decades. In sharp contrast to the progress on the level of model
uncertainty is prioritisation of prediction studies still generally based upon ‘expert judgement’. Very
few studies have focussed on the question how uncertainty of predictions on the composition of
pumped groundwater should be used for management decisions on research priorities. However,
deciding on these research strategies has become more complex, due to the increased size and
interdependency of regional drinking water supply systems. Consequently, there is a need for decision
support methods in order to avoid sub-optimal strategies.
This report presents a framework that is based on the above-mentioned methodology for multipleobjective
optimisation of drinking water production. It enables decision support for allocation of
research priorities to groundwater quality prediction studies. Rational research strategies on
groundwater quality prediction seek to minimize the risk of well failure due to contamination of
groundwater (breakthrough). There are 3 elements that form the basis of our approach:
• the quantification of risks for drinking water supply due to groundwater pollution
• an operational quantification of the reliability of predictions
• the anticipated marginal precision efficiency of additional prediction studies
The minimal negative impact of well failure in both economic and environmental terms is assessed by
using genetic algorithms
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