{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:10:02Z","timestamp":1773382202631,"version":"3.50.1"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42075141"],"award-info":[{"award-number":["42075141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004204","name":"Model Interdisciplinary Joint Research Projects of Tongji University in 2021","doi-asserted-by":"publisher","award":["YB-21-202110"],"award-info":[{"award-number":["YB-21-202110"]}],"id":[{"id":"10.13039\/501100004204","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project Fund of Shanghai 2020 \u201cScience and Technology Innovation Action Plan\u201d for Social Development","award":["20dz1200702"],"award-info":[{"award-number":["20dz1200702"]}]},{"DOI":"10.13039\/501100001809","name":"Meteorological Joint Funds of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2142211"],"award-info":[{"award-number":["U2142211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0608000"],"award-info":[{"award-number":["2020YFA0608000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Geosci. Remote Sensing Lett."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/lgrs.2023.3250642","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T18:29:34Z","timestamp":1677695374000},"page":"1-5","source":"Crossref","is-referenced-by-count":14,"title":["PIRT: A Physics-Informed Red Tide Deep Learning Forecast Model Considering Causal-Inferred Predictors Selection"],"prefix":"10.1109","volume":"20","author":[{"given":"Bin","family":"Mu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7093-6531","authenticated-orcid":false,"given":"Bo","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-3137","authenticated-orcid":false,"given":"Shijin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9297-7954","authenticated-orcid":false,"given":"Yuxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-013-0457-1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2015.09.002"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5194\/bg-7-621-2010"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2019.109923"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03242-8"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.124488"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.2495\/EID180141"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2022.109913"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2022.118591"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-2414-1_34"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2018.1553742"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT.2019.8702027"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.06.015"},{"key":"ref14","first-page":"7154","article-title":"DAG-GNN: DAG structure learning with graph neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"issue":"5","key":"ref15","first-page":"23","article-title":"Analysis of spatial and temporal characteristics of chlorophyll-a concentration and red tide monitoring in Bohai Sea","volume":"42","author":"Jiang","year":"2018","journal-title":"Mar. Sci."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecss.2016.02.016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1029\/2005GL025431"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"ref19","volume-title":"Elements of Causal Inference: Foundations and Learning Algorithms","author":"Peters","year":"2017"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref21","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xingjian"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"issue":"6","key":"ref23","first-page":"39","article-title":"Change conalysis of chlorophyll concentration in the East China Sea and its response to seawater temperature","author":"Shan","year":"2020","journal-title":"Bull. Surv. Mapping"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252470"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/lgrs.2017.2733548"},{"key":"ref26","first-page":"879","article-title":"PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01149"}],"container-title":["IEEE Geoscience and Remote Sensing Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8859\/10034981\/10056967.pdf?arnumber=10056967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T23:55:20Z","timestamp":1709423720000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10056967\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/lgrs.2023.3250642","relation":{},"ISSN":["1545-598X","1558-0571"],"issn-type":[{"value":"1545-598X","type":"print"},{"value":"1558-0571","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}