An artificial intelligence approach for managing water demand in water supply systems. Issue 1 (1st January 2023)

• Record Type: Journal Article
• Authors: Zanfei, Ariele; Menapace, Andrea; Righetti, Maurizio
• Is Part Of: IOP conference series. Volume 1136:Issue 1(2023)
• Publication Date: 2023-01-01
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/1136/1/012004
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Water demand management is essential for water utilities, which have the critical task of supplying drinking water from water sources to end-users through the distribution network. Therefore, the water utilities have to make decisions for the current and future functioning of the water distribution system. In this context, the artificial intelligence approach with data-driven methods can be used to develop powerful tools to improve overall water management. In fact, data-driven methods can model water demands for plenty of tasks and applications such as demand forecasting or anomaly detection. In this work, we propose and discuss a practical application of an artificial neural network to model the urban water demand of a water supply system. The flexibility of the proposed method allows the prediction of water demand on different horizons. Moreover, this developed model can effectively support water utilities on different operational schedules and decision tasks.