Water, Volume 14, Issue 4
2022 February-2 - 168 articles
Cover Story: Water distribution system monitoring needs advanced real-time control technologies to achieve maximum operational efficiency. This work proposes a state estimation methodology that enables inferring the operating speed of the system’s pumping stations from the monitored pressure and flow rate measurements across the system. The approach uses graph convolutional neural network theory linked to hydraulic models that generate a digital twin of the water system. It is validated on two benchmark hydraulic networks, where the proposed model effectively predicts the system’s state. The results of the evaluation metrics reflect a high predictive ability and that the prediction results adequately represent real data. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
- You may sign up for email alerts to receive table of contents of newly released issues.
- PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.