Abstract
The aim of the industrially situated research was to examine the decision-making processes in the supply of water within large urban areas and Megacities, where problems of water stress are particularly acute, in order to understand and hopefully improve the methods used with the objective of enabling changes to reduce or minimise negative environmental impacts, by the development of decision-support tools or methodologies. The issues of social and economic water stress and environmental degradation through urbanisation are particularly severe in developing countries, but there is a need to provide long-term solutions that are sustainable, rather than reactively engineering the way out of crises. Mechanisms are needed that can offer methods to rebalance the environmental, economic and social dimensions of water management. Through synthesis of literature, expert knowledge and experimental research, the following proposal is made. Decisions regarding the management of water should involve engineers and managers whose responsibilities it is to maintain adequate water supplies and protect the aquatic resources, and also the users of water (the customers) who form the main stakeholder group. To adequately adopt demand management and sustainability principles, it is necessary to integrate societal information (particularly that generated by the users themselves) into the range of information and data typically available to the water professionals for the purposes of strategic and routine management of supplies. In order to address this, a range of tools must be developed that are able to deal with the inherent uncertainties related to the measurement methods and the dynamic nature contained within such data. Part of the process of managing uncertainty in societal data is systematic collection, and use of quantitative analysis methods that are adapted for coping with fuzzy information. Quantitative attitudinal data and qualitative focus group style interview data has been collected and analysed in a novel way through using corpus linguistic techniques. The results show that unique and exploitable knowledge regarding demand patterns for water, and likely water usage behaviour, of user stakeholders can be rapidly obtained in this way.