Abstract
Constructing Sustainable Smart Water Supply systems are facing serious challenges all around the world with the fast expansion of modern cities. Water quality is influencing our life ubiquitously and prioritizing all the urban management. Traditional urban water quality control mostly focused on routine tests of quality indicators, which include physical, chemical and biological groups. However, the inevitable delay for biological indicators has increased the health risk and leads to accidents such as massive infections in many big cities. In this paper, we first analyse the problem, technical challenges, and research questions. Then we provide a possible solution by building a risk analysis framework for the urban water supply system. It takes indicator data we collected from industrial processes to perceive water quality changes, and further for risk detection. In order to provide explainable results, we propose an Adaptive Frequency Analysis (Adp-FA) method to resolve the data using indicators’ frequency domain information for their inner relationships and individual prediction. We also investigate the scalability properties of this method from indicator, geography and time domains. For the application, we select industrial quality data sets collected from a Norwegian project in 4 different urban water supply systems, as Oslo, Bergen, Strømmen and Aalesund.
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