The endogenous complexity and spatial nature of the problems encountered in the urban water management environment present decision-makers with three major problems: (a) in the urban environment, every decision is site-specific, almost on a case-by-case basis, (b) the decision-maker must access, simultaneously, a large amount of information, increasing with rising spatial resolution and (c) the information to be evaluated is heterogeneous, including engineering, economical and social characteristics and constraints. The first two problems indicate that urban water management is an ideal field to develop and use spatial decision support systems (SDSS), while the latter promotes the use of fuzzy inference systems as a key mathematical framework. This research discusses the nature of uncertainty in environmental management in general and urban water management in particular, argues that fuzzy, rule-based, inference systems can be an invaluable tool for uncertainty quantification and presents the relevant elements of a prototype SDSS for urban water management. The examples presented in this paper are based on an application of the SDSS in water demand management.

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