{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:04:03Z","timestamp":1772042643863,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia. I.P. (Portuguese Foundation for Science and Technology)","award":["UIDB\/05064\/2020"],"award-info":[{"award-number":["UIDB\/05064\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Water scarcity poses a significant challenge to social integration and economic development, necessitating efficient water management strategies. This study compares time series forecasting models, both classical, Holt\u2013Winters and ARIMA, and modern, LSTM and Prophet, to determine the most accurate model for predicting water flow in public supply networks. Data from four rural Portuguese locations were used, with preprocessing ensuring quality and uniformity. Performance metrics were evaluated for both medium-term (10 days) and long-term (3 months) forecasts. Results indicate that classical models like Holt\u2013Winters and ARIMA perform better for medium-term predictions, while modern models, particularly LSTM, excel in long-term forecasts by effectively capturing seasonal patterns. Future research should integrate additional variables and explore hybrid models to enhance forecasting accuracy.<\/jats:p>","DOI":"10.3390\/w16131827","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T04:19:26Z","timestamp":1719461966000},"page":"1827","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing Water Management: A Comparative Analysis of Time Series Prediction Models for Distributed Water Flow in Supply Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Carlos","family":"Pires","sequence":"first","affiliation":[{"name":"Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal"},{"name":"FCC Aqualia Portugal, 1990-514 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1358-8638","authenticated-orcid":false,"given":"M\u00f3nica V.","family":"Martins","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal"},{"name":"VALORIZA\u2014Research Center for Endogenous Resource Valorization, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tzanakakis, V.A., Paranychianakis, N.V., and Angelakis, A.N. 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