Evaluation of public perception on key sustainability indicators for drinking water quality by fuzzy logic methodologies

Social acceptance of water quality in urban areas depends on the perception and awareness of the stakeholders and the wider public. Moreover, the significance of social trust in stakeholders appears to be an influential factor affecting social support of water quality. However, efforts on understanding, communicating and engaging with the public and the stakeholder community still remain under examination. Urban water resources management problems are often associated with ecological, political, social and economic development and have caused critical concern to both national and local competent authorities in almost every country for many years. When focusing on water quality, key indicators are used to measure the performance (KPIs) of water companies in evaluating their success to customer satisfaction with zero defects while minimizing the environmental footprint caused by the intermediate procedures up to the point of receiving potable tap water. Stakeholder interviews reveal that these KPIs can be identified from an economic, social, environmental and water company perspective, and they can span from the efficiency of water distribution to operational water losses minimization procedures and from customer supply coverage to aesthetic test compliance. Therefore, future policy for water quality should be designed in a strategic framework, taking into consideration key sustainability indicators emanating from the aforementioned perspectives. In the current paper, a complete showcase is illustrated on how to mobilize local stakeholder's knowledge to extract KPIs for supporting effective strategies by local water companies and initiating policy making by the competent authorities in the process of keeping high water quality standards in a sustainable water resources management environment. Specifically, the application of soft computing methods enables the conceptualization and categorization of stakeholders' notion of strategies that need to be followed. This conceptualization allowed the involvement of fuzzy inference systems to simulate the effects of several policies in a multi-criteria analysis. According to the features, the policy maker initiates, the proposed model succeeds to identify the preferred policy options that can be used in achieving minimal environmental footprint.