{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T08:09:39Z","timestamp":1776240579849,"version":"3.50.1"},"reference-count":64,"publisher":"Informa UK Limited","issue":"7","funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB3900903"],"award-info":[{"award-number":["2021YFB3900903"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Geographical Information Science"],"published-print":{"date-parts":[[2024,7,2]]},"DOI":"10.1080\/13658816.2024.2342321","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T11:14:00Z","timestamp":1713784440000},"page":"1207-1231","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":11,"title":["Physics-guided spatio\u2013temporal neural network for predicting dissolved oxygen concentration in rivers"],"prefix":"10.1080","volume":"38","author":[{"given":"Qiliang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Geo-informatics, Central South University, Changsha, Hunan, P.R. China"}]},{"given":"Yuzhao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geo-informatics, Central South University, Changsha, Hunan, P.R. China"},{"name":"Guangzhou Urban Planning &amp; Design Survey Research Institute Co., Ltd, Guangzhou, Guangdong, P.R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9368-5306","authenticated-orcid":false,"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geo-informatics, Central South University, Changsha, Hunan, P.R. China"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Geo-informatics, Central South University, Changsha, Hunan, P.R. China"}]},{"given":"Junjie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geo-informatics, Central South University, Changsha, Hunan, P.R. China"}]},{"given":"Keyi","family":"An","sequence":"additional","affiliation":[{"name":"Department of Geo-informatics, Central South University, Changsha, Hunan, P.R. China"}]}],"member":"301","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.124084"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.7312\/alle06918"},{"key":"e_1_3_4_4_1","volume-title":"Standard methods for examination of water and wastewater","author":"American Public Health Association (APHA),","year":"2005","unstructured":"American Public Health Association (APHA), 2005. Standard methods for examination of water and wastewater. 21st ed. Washington, DC: American Public Health Association, American Water Works Association, Water Environment Federation.","edition":"21"},{"key":"e_1_3_4_5_1","unstructured":"Ba Y. Zhao G. and Kadambi A. 2019. Blending diverse physical priors with neural networks. arXiv preprint arXiv:1910.00201."},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/w12123399"},{"key":"e_1_3_4_7_1","first-page":"1171","volume-title":"Advances in neural information processing systems","author":"Bengio S.","year":"2015","unstructured":"Bengio, S., et\u00a0al., 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In: C. Cortes, N.D. Lawrence, D.D. Lee, et al., eds. Advances in neural information processing systems. Cambridge, MA: MIT Press, 1171\u20131179."},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3064029"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/09640568.2020.1776227"},{"key":"e_1_3_4_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3507548.3507597"},{"key":"e_1_3_4_11_1","unstructured":"Chung J. et\u00a0al. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555."},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/2015WR017910"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0048-9697(03)00062-7"},{"key":"e_1_3_4_14_1","first-page":"532","volume-title":"In","author":"Daw A.","year":"2020","unstructured":"Daw, A., et\u00a0al., 2020. Physics-guided architecture (PGA) of neural networks for quantifying uncertainty in lake temperature modeling. In: C. Demeniconi, et al. Proceedings of the 2020 SIAM international conference on data mining, 7\u20139 May 2020. Cincinnati, OH: Society for Industrial and Applied Mathematics, 532\u2013540."},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3152818"},{"issue":"2","key":"e_1_3_4_16_1","first-page":"87","article-title":"Gene expression programming: a new adaptive algorithm for solving problems","volume":"13","author":"Ferreira C.","year":"2001","unstructured":"Ferreira, C., 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, 13 (2), 87\u2013129.","journal-title":"Complex Systems"},{"key":"e_1_3_4_17_1","doi-asserted-by":"publisher","DOI":"10.2478\/johh-2013-0020"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)HE.1943-5584.0000044"},{"key":"e_1_3_4_19_1","unstructured":"Hamrick J.M. 1996. User\u2019s manual for the environmental fluid dynamics computer code. Special Reports in Applied Marine Science and Ocean Engineering (SRAMSOE) No. 331. Virginia Institute of Marine Science College of William and Mary."},{"key":"e_1_3_4_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2020.109136"},{"key":"e_1_3_4_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.05.016"},{"key":"e_1_3_4_22_1","first-page":"558","volume-title":"In","author":"Jia X.","year":"2019","unstructured":"Jia, X., et\u00a0al., 2019. Physics guided RNNs for modeling dynamical systems: a case study in simulating lake temperature profiles. In: T.Y. Berger-Wolf and N.V. Chawla, eds. Proceedings of the 2019 SIAM international conference on data mining, 2\u20134 May 2019. Calgary, Alberta, Canada: Society for Industrial and Applied Mathematics, 558\u2013566."},{"key":"e_1_3_4_23_1","first-page":"612","volume-title":"In","author":"Jia X.","year":"2021","unstructured":"Jia, X., et\u00a0al., 2021. Physics-guided recurrent graph model for predicting flow and temperature in river networks. In: T.C. Demeniconi, et al., eds. Proceedings of the 2021 SIAM international conference on data mining (SDM), April 29\u2013May 1 2021. Cincinnati, OH: Society for Industrial and Applied Mathematics, 612\u2013620."},{"key":"e_1_3_4_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-019-06049-2"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.3133\/pp1136"},{"key":"e_1_3_4_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-006-9505-1"},{"key":"e_1_3_4_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2012.02.014"},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"key":"e_1_3_4_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2720168"},{"key":"e_1_3_4_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-2917-8"},{"key":"e_1_3_4_31_1","doi-asserted-by":"publisher","DOI":"10.1029\/2020WR029188"},{"key":"e_1_3_4_32_1","doi-asserted-by":"publisher","DOI":"10.3808\/jei.201300248"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/2014WR015402"},{"key":"e_1_3_4_34_1","first-page":"11906","volume-title":"Proceedings of International conference on machine learning","author":"Lan S.","year":"2022","unstructured":"Lan, S., et\u00a0al., 2022. DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: Proceedings of International conference on machine learning, 17\u201323 July 2022 Baltimore, Maryland, USA. New York, NY: Association for Computing Machinery, 11906\u201311917."},{"key":"e_1_3_4_35_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1248506"},{"key":"e_1_3_4_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2020.126673"},{"key":"e_1_3_4_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.139099"},{"key":"e_1_3_4_38_1","first-page":"1","volume-title":"Proceedings of the 6th international conference on learning representations","author":"Li Y.","year":"2018","unstructured":"Li, Y., et\u00a0al., 2018. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: Proceedings of the 6th international conference on learning representations, 30 April\u20133 May 2018. Vancouver, Canada: International Society of the Learning Sciences, 1\u201316."},{"key":"e_1_3_4_39_1","first-page":"13","article-title":"GeoMAN: multi-level attention networks for geo-sensory time series prediction","author":"Liang Y.","year":"2018","unstructured":"Liang, Y., et\u00a0al., 2018. GeoMAN: multi-level attention networks for geo-sensory time series prediction. In: International joint conference on artificial intelligence, 13\u201319 July 2018 Stockholm, Sweden. Washington DC: AAAI Press, 3428\u20133434.","journal-title":"In:"},{"key":"e_1_3_4_40_1","doi-asserted-by":"publisher","DOI":"10.1029\/96WR03529"},{"key":"e_1_3_4_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-019-05553-9"},{"key":"e_1_3_4_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2013.08.016"},{"key":"e_1_3_4_43_1","first-page":"1","volume-title":"In","author":"Moshe Z.","year":"2020","unstructured":"Moshe, Z., et\u00a0al., 2020. HydroNets: leveraging river structure for hydrologic modeling. In: Proceedings of the eighth international conference on learning representations, 26\u201330 April 2020 Addis Ababa, Ethiopia. Appleton, WI: International Society of the Learning Sciences, 1\u20138."},{"key":"e_1_3_4_44_1","first-page":"559","volume-title":"In","author":"Muralidhar N.","year":"2020","unstructured":"Muralidhar, N., et\u00a0al., 2020. PhyNet: physics guided neural networks for particle drag force prediction in assembly. In: Proceedings of the 2020 SIAM international conference on data mining, 7\u20139 May 2020. Cincinnati, OH: Society for Industrial and Applied Mathematics, 559\u2013567."},{"key":"e_1_3_4_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10329-8"},{"key":"e_1_3_4_46_1","article-title":"An improved graph convolutional network with feature and temporal attention for multivariate water quality prediction","volume":"14","author":"Ni Q.J.","year":"2023","unstructured":"Ni, Q.J., et\u00a0al., 2023. An improved graph convolutional network with feature and temporal attention for multivariate water quality prediction. Environmental Science and Pollution Research, 14, 11516\u201311529.","journal-title":"Environmental Science and Pollution Research"},{"key":"e_1_3_4_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/JOE.1980.1145442"},{"key":"e_1_3_4_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2009.12.023"},{"key":"e_1_3_4_49_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1"},{"key":"e_1_3_4_50_1","volume-title":"Standard methods for the examination of water and wastewater","author":"Rice E.W.","year":"2012","unstructured":"Rice, E.W., et al., 2012. Standard methods for the examination of water and wastewater. 22nd ed. Washington, DC: American Public Health Association."},{"key":"e_1_3_4_51_1","unstructured":"Roesner L.A. Giguere P.R. and Evenson D.E. 1977. Computer program documentation for the stream quality model QUAL-II. Athens GA: US Environmental Protection Agency."},{"key":"e_1_3_4_52_1","doi-asserted-by":"publisher","DOI":"10.5194\/acp-15-4399-2015"},{"key":"e_1_3_4_53_1","unstructured":"Streeter H.W. and Phelps E.B. 1958. A study of the pollution and natural purification of the Ohio River. Washington DC: US Department of Health Education & Welfare."},{"key":"e_1_3_4_54_1","first-page":"2, 3104\u20133112","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever I.","year":"2014","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V., 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 2, 3104\u20133112.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_55_1","doi-asserted-by":"publisher","DOI":"10.1002\/jctb.5010050201"},{"key":"e_1_3_4_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.124250"},{"key":"e_1_3_4_57_1","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.14565"},{"key":"e_1_3_4_58_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature09440"},{"key":"e_1_3_4_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2022.128332"},{"key":"e_1_3_4_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/0266-9838(92)90006-P"},{"key":"e_1_3_4_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514228"},{"key":"e_1_3_4_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11270-010-0695-3"},{"issue":"01","key":"e_1_3_4_63_1","first-page":"1234","article-title":"GMAN: a graph multi-attention network for traffic prediction","volume":"34","author":"Zheng C.","year":"2020","unstructured":"Zheng, C., et\u00a0al., 2020. GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, 34 (01), 1234\u20131241.","journal-title":"In"},{"key":"e_1_3_4_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.024"},{"key":"e_1_3_4_65_1","doi-asserted-by":"publisher","DOI":"10.1111\/wej.12630"}],"container-title":["International Journal of Geographical Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/13658816.2024.2342321","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T13:29:28Z","timestamp":1727184568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/13658816.2024.2342321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":64,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7,2]]}},"alternative-id":["10.1080\/13658816.2024.2342321"],"URL":"https:\/\/doi.org\/10.1080\/13658816.2024.2342321","relation":{},"ISSN":["1365-8816","1362-3087"],"issn-type":[{"value":"1365-8816","type":"print"},{"value":"1362-3087","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,22]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tgis20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tgis20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2023-06-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-08","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}