The DayWater multi-criteria comparator.
Ellis, John Bryan and Revitt, D. Mike and Scholes, Lian N. L. (2008) The DayWater multi-criteria comparator. In: DayWater: an Adaptive Decision Support System for Urban Stormwater Management. Thevenot, Daniel R, ed. IWA Publishing, London, pp. 87-96. ISBN 9781843391609
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Multi-criteria analysis (MCA) is a decision-making tool originally developed for the evaluation of options where not all of the considerations have an easily assignable value. It may be considered as an extension of the more traditional decision-making techniques (e.g. cost-benefit analysis) in that, as well as financial information, it also enables a range of other factors, such as the environmental and social impacts of the possible alternatives to be taken into account. The resolution of best practice source strategies requires an integrated evaluation of such environmental, technological, economic and social values which are normally set in a local planning and/or site master-scenario context, and which is inevitably inter-disciplinary and inter-institutional in character. Any resolving optimisation procedure must be capable of addressing dimensions of multi-criteria complexity in a manner which is both understandable and auditable to the variety of stakeholders and, if possible, expressed in a simple but objectively quantified methodology. MCA provides such an optimisation approach that facilitates transparent and auditable decision-making, enabling the views and concerns of all stakeholders to be taken into account in combination with scientific and technological data. However, the variety of stakeholders (or “deciders”) will normally also hold different opinions (or strategies) regarding the significance (or ‘weight’) that might be attributed to various influencing criteria and Key Performance Indicators (KPIs) although weighting procedures within MCA methodologies can be contentious. There are several types of MCA but all approaches involve the development of a performance matrix containing data/information on how a range of possible solutions perform in relation to a variety of criteria and/or indicators.
|Item Type:||Book Section|
|Research Areas:||A. > School of Science and Technology > Natural Sciences|
|Depositing User:||Mike Revitt|
|Date Deposited:||26 Oct 2009 13:45|
|Last Modified:||13 Oct 2016 14:15|
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