{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T12:47:17Z","timestamp":1774788437373,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Urban water resource planning as well as the supply-demand balance are directly affected by immediate demand forecasts, making them vital for water resource management.\u00a0\u00a0 The present approaches to water demand forecasting just consider temporal variables and do not take account of the possible impact of geographical characteristics on them.\u00a0 The reason for this is because short-term urban water demand forecasts are affected by several variables, many of which display complex nonlinear dynamic features.\u00a0\u00a0 Predictions end up being inaccurate because of this.\u00a0\u00a0 This research aims to address this problem by presenting a model that considers geographical and temporal characteristics in order to predict urban water consumption in the near term.\u00a0\u00a0 The first step is to detect and fix anomalies using the Prophet model.\u00a0\u00a0 In order to generate an adjacency matrix among variables, we use a maximum information coefficient, and to extract spatial attributes between variables, we use a graph convolutional neural network.\u00a0 Afterwards, a multi-head attention method is used to enhance crucial aspects of water consumption statistics while reducing the influence of unimportant components.\u00a0\u00a0 The next phase involves projecting urban areas' short-term water demands using a three-layer long short-term memory system.\u00a0\u00a0 This study's proposed hybrid model outperforms state-of-the-art prediction methods in terms of accuracy and efficiency, with an average percentage absolute error reduction of 1.868-2.718%.\u00a0\u00a0 Not only does this study set the stage for future research, it also has the potential to aid cities in making better use of their water resources.<\/jats:p>","DOI":"10.31449\/inf.v50i8.10142","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T11:59:46Z","timestamp":1771761586000},"source":"Crossref","is-referenced-by-count":0,"title":["GCN-GRU: Multi step prediction model for urban water consumption by integrating spatiotemporal graph convolution and multi head attention mechanism"],"prefix":"10.31449","volume":"50","author":[{"given":"Muhua","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10142\/6542","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10142\/6542","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T11:57:31Z","timestamp":1774785451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i8.10142","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}