Your search found 1 records
1 Zanfei, A.; Lombardi, A.; De Luca, A.; Menapace, A. 2024. AI-forecast: an innovative and practical tool for short-term water demand forecasting. Water Supply, ws2024055. (Online first) [doi: https://doi.org/10.2166/ws.2024.055]
Water demand ; Forecasting ; Neural networks ; Machine learning ; Innovation ; Water distribution systems ; Water management ; Models ; Case studies
(Location: IWMI HQ Call no: e-copy only Record No: H052719)
https://iwaponline.com/ws/article-pdf/doi/10.2166/ws.2024.055/1382707/ws2024055.pdf
https://vlibrary.iwmi.org/pdf/H052719.pdf
https://vlibrary.iwmi.org/pdf/H052719.pdf
(0.82 MB) (836 KB)
Water management is a major contemporary and future challenge. In an increasing water demand scenario related to climate change, a water distribution system must ensure equal access to water for all users. In this context, a reliable short-term water demand forecasting system is crucial for reliable water management. However, despite the abundance of studies in the scientific literature, few examples highlight complete tools for providing such models to real water utilities and water managers. This study presents AI-Forecast, an innovative tool developed to predict water demand with state-of-art models. Such tool is based on the data-driven logic, and it is designed to provide a complete data-driven chain that starts from the data and arrives to the short-term water demand prediction. AI-Forecast can import data, properly manage them, and assess tasks like outlier detection and missing data imputation. Eventually, it can implement state-of-the-art forecasting models and provide the forecasts. The prediction is shown through an intuitive web interface, which is designed to highlight the major information related to the prediction accuracy. Although this tool does not provide a new prediction algorithm, it proposes a complete data-driven chain that is practically designed to take such models in practice to real water utilities.
Powered by DB/Text WebPublisher, from
