WATERNET A NETWORK FOR MONITORING AND ASSESSING WATER QUALITY FOR DRINKING AND IRRIGATION PURPOSES
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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 analyze 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 (AdpFA) 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.