{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T12:53:16Z","timestamp":1777121596031,"version":"3.51.4"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"DOI":"10.13039\/501100001809","name":"the Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61402217"],"award-info":[{"award-number":["61402217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang public welfare program of applied research","award":["LGF19F020012"],"award-info":[{"award-number":["LGF19F020012"]}]},{"name":"Youth Foundation of Social Science and Humanity, Ministry of Education of China","award":["20YJCZH077"],"award-info":[{"award-number":["20YJCZH077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:p> By using of Gated Recurrent Unit and Graph Convolution network model, this work, based on remote sensing water quality measurement data, forecast the water quality which observed by the index of the chlorophyll A value and total nitrogen at the chosen observation points. The data is decomposed and reconstructed in time series in the light of the spatio-temporal correlation. The dependence of the processed sample is obviously decreased. The prediction results reflect the temporal and spatial changes of water quality data, and have faster processing speed and stronger computing power than other models. <\/jats:p>","DOI":"10.1142\/s021821302250018x","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T08:07:21Z","timestamp":1645690041000},"source":"Crossref","is-referenced-by-count":9,"title":["Research on Application of Graph Neural Network in Water Quality Prediction"],"prefix":"10.1142","volume":"31","author":[{"given":"Lan","family":"Li","sequence":"first","affiliation":[{"name":"Zhejiang University of Water Resources and Electric Power, Hangzhou 33333, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nanbin","family":"Lv","sequence":"additional","affiliation":[{"name":"Zhejiang University of Water Resources and Electric Power, Hangzhou 33333, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University of Water Resources and Electric Power, Hangzhou 33333, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,2,28]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021821302250018X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T01:06:55Z","timestamp":1646010415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S021821302250018X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":0,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["10.1142\/S021821302250018X"],"URL":"https:\/\/doi.org\/10.1142\/s021821302250018x","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2]]},"article-number":"2250018"}}