Quality Risk Analysis for Sustainable Smart Water Supply

With the rapid growth of contemporary cities, building sustainable smart water supply systems is facing significant hurdles worldwide. Water quality has a impact on every aspect our lives and given priority in urban planning. Previous methods regulating have mostly relied performing routine tests several physical, chemical, biological groupings indicators. However, inevitable lag time for indicators raised risk illness, resulting mishaps like widespread infections many major cities. In this essay, we examine issue, technological difficulties, research questions. Next, offer potential remedy by creating methodology analysis specific to delivery system. To detect risks changes quality, need indicator data gathered from industrial activities. We provide an Adaptive Frequency Analysis (Adp- FA) method resolve using indicators' frequency domain information their internal linkages individual prediction order produce results that are understandable. also look into method's scalability indicator, geographic, domains. choose sets application was as part Norwegian project four separate systems: Oslo, Bergen, Strmmen, Lesund. Comparing new traditional Artificial Neural Network Random Forest approach, proposed spectrogram, accuracy, requirements. The outcomes demonstrate strategy performs better most areas. Supporting early alerts additional decision support possible. Key Words: Quality, Risk Analysis, Industrial Quality Data, Indicators