{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T19:24:02Z","timestamp":1720466642976},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2020-0-00843","RS-2023-00223446"],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.engappai.2023.107718","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T10:21:59Z","timestamp":1704709319000},"page":"107718","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Two-pathway spatiotemporal representation learning for extreme water temperature prediction"],"prefix":"10.1016","volume":"131","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8110-6047","authenticated-orcid":false,"given":"Jinah","family":"Kim","sequence":"first","affiliation":[]},{"given":"Taekyung","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Jaeil","family":"Kim","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2023.107718_b1","article-title":"Argo float data and metadata from global data assembly centre (Argo GDAC)","author":"Argo","year":"2000","journal-title":"Seanoe"},{"key":"10.1016\/j.engappai.2023.107718_b2","doi-asserted-by":"crossref","unstructured":"Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lu\u010di\u0107, M., Schmid, C., 2021. Vivit: A video vision transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 6836\u20136846.","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"10.1016\/j.engappai.2023.107718_b3","first-page":"213","article-title":"The accuracy of satellite-composite GHRSST and model-reanalysis sea surface temperature data at the seas adjacent to the Korean Peninsula","volume":"41","author":"Baek","year":"2019","journal-title":"Ocean Polar Res."},{"key":"10.1016\/j.engappai.2023.107718_b4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.pocean.2004.06.005","article-title":"Circulation and currents in the southwestern East\/Japan Sea: Overview and review","volume":"61","author":"Chang","year":"2004","journal-title":"Prog. Oceanogr."},{"key":"10.1016\/j.engappai.2023.107718_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsr2.2023.105262","article-title":"Deep-learning model for sea surface temperature prediction near the Korean Peninsula","volume":"208","author":"Choi","year":"2023","journal-title":"Deep Sea Res. II"},{"key":"10.1016\/j.engappai.2023.107718_b6","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2010.10.017","article-title":"The operational sea surface temperature and sea ice analysis (OSTIA) system","volume":"116","author":"Donlon","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.engappai.2023.107718_b7","series-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.engappai.2023.107718_b8","series-title":"Global context vision transformers","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.engappai.2023.107718_b9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.engappai.2023.107718_b10","series-title":"Gaussian error linear units (gelus)","author":"Hendrycks","year":"2016"},{"key":"10.1016\/j.engappai.2023.107718_b11","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"10.1016\/j.engappai.2023.107718_b12","doi-asserted-by":"crossref","first-page":"12514","DOI":"10.1109\/JSTARS.2021.3128577","article-title":"D2cl: A dense dilated convolutional lstm model for sea surface temperature prediction","volume":"14","author":"Hou","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2023.107718_b13","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al., 2019. Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10.1016\/j.engappai.2023.107718_b14","series-title":"Provable benefit of orthogonal initialization in optimizing deep linear networks","author":"Hu","year":"2020"},{"key":"10.1016\/j.engappai.2023.107718_b15","doi-asserted-by":"crossref","first-page":"9687","DOI":"10.1029\/JC087iC12p09687","article-title":"Spring season flow of the Tsushima Current and its separation from the Kuroshio: Satellite evidence","volume":"87","author":"Huh","year":"1982","journal-title":"J. Geophys. Res.: Oceans"},{"key":"10.1016\/j.engappai.2023.107718_b16","series-title":"Data stewardship maturity report for GHRSST level 4 OSTIA global foundation sea surface temperature analysis (GDS versions 1 and 2)","author":"Ionin","year":"2021"},{"key":"10.1016\/j.engappai.2023.107718_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3098425","article-title":"Sea surface temperature forecasting with ensemble of stacked deep neural networks","volume":"19","author":"Jahanbakht","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105675","article-title":"Assessment of the spatiotemporal prediction capabilities of machine learning algorithms on sea surface temperature data: A comprehensive study","volume":"118","author":"Kartal","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2023.107718_b19","series-title":"International Conference on Algorithmic Learning Theory","first-page":"597","article-title":"On the computational complexity of self-attention","author":"Keles","year":"2023"},{"key":"10.1016\/j.engappai.2023.107718_b20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.engappai.2023.107718_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106854","article-title":"Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature","volume":"126","author":"Kim","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2023.107718_b22","series-title":"Adam: A method for stochastic optimization. ICLR. 2015","author":"Kingma","year":"2015"},{"key":"10.1016\/j.engappai.2023.107718_b23","series-title":"Report on Ocean Climate Analysis(1981-2020)","author":"KMA","year":"2021"},{"key":"10.1016\/j.engappai.2023.107718_b24","doi-asserted-by":"crossref","DOI":"10.1029\/2005GL023026","article-title":"Decadal change in relationship between east Asian and WNP summer monsoons","volume":"32","author":"Kwon","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b25","doi-asserted-by":"crossref","DOI":"10.1029\/2010GL044353","article-title":"Contrasting evolution of sea surface temperature in the benguela upwelling system under natural and anthropogenic climate forcings","volume":"37","author":"Leduc","year":"2010","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b26","doi-asserted-by":"crossref","first-page":"15679","DOI":"10.1029\/1999JC900108","article-title":"Influence of stratification on residual tidal currents in the Yellow Sea","volume":"104","author":"Lee","year":"1999","journal-title":"J. Geophys. Res.: Oceans"},{"key":"10.1016\/j.engappai.2023.107718_b27","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, J., Willette, J., Hwang, S.J., 2022a. MPViT: Multi-path vision transformer for dense prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7287\u20137296.","DOI":"10.1109\/CVPR52688.2022.00714"},{"key":"10.1016\/j.engappai.2023.107718_b28","first-page":"17","article-title":"Record-breaking high temperature in July 2021 over East sea and possible mechanism","volume":"32","author":"Lee","year":"2022","journal-title":"Atmosphere"},{"key":"10.1016\/j.engappai.2023.107718_b29","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/ab8527","article-title":"Two major modes of East Asian marine heatwaves","volume":"15","author":"Lee","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b30","doi-asserted-by":"crossref","DOI":"10.1029\/2023JC019761","article-title":"Rapidly changing East Asian marine heatwaves under a warming climate","author":"Lee","year":"2023","journal-title":"J. Geophys. Res.: Oceans"},{"key":"10.1016\/j.engappai.2023.107718_b31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.engappai.2023.107718_b32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H., 2022. Video swin transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3202\u20133211.","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"10.1016\/j.engappai.2023.107718_b33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41612-018-0014-z","article-title":"Progress in subseasonal to seasonal prediction through a joint weather and climate community effort","volume":"1","author":"Mariotti","year":"2018","journal-title":"npj Clim. Atmos. Sci."},{"key":"10.1016\/j.engappai.2023.107718_b34","series-title":"International Conference on Machine Learning","first-page":"2401","article-title":"Efficient orthogonal parametrisation of recurrent neural networks using householder reflections","author":"Mhammedi","year":"2017"},{"key":"10.1016\/j.engappai.2023.107718_b35","doi-asserted-by":"crossref","first-page":"191","DOI":"10.5670\/oceanog.2013.27","article-title":"Fisheries management in a changing climate: Lessons from the 2012 ocean heat wave in the Northwest Atlantic","volume":"26","author":"Mills","year":"2013","journal-title":"Oceanography"},{"key":"10.1016\/j.engappai.2023.107718_b36","doi-asserted-by":"crossref","first-page":"415","DOI":"10.4217\/OPR.2006.28.4.415","article-title":"Interannual variability and long-term trend of coastal sea surface temperature in Korea","volume":"28","author":"Min","year":"2006","journal-title":"Ocean Polar Res."},{"key":"10.1016\/j.engappai.2023.107718_b37","series-title":"GHRSST level 4 AVHRR OI global blended sea surface temperature analysis","author":"NCEI","year":"2016"},{"key":"10.1016\/j.engappai.2023.107718_b38","doi-asserted-by":"crossref","first-page":"420","DOI":"10.3389\/fmars.2019.00420","article-title":"Observational needs of sea surface temperature","volume":"6","author":"O\u2019Carroll","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"10.1016\/j.engappai.2023.107718_b39","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1146\/annurev-marine-032720-095144","article-title":"Marine heatwaves","volume":"13","author":"Oliver","year":"2021","journal-title":"Ann. Rev. Mar. Sci."},{"key":"10.1016\/j.engappai.2023.107718_b40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-03732-9","article-title":"Longer and more frequent marine heatwaves over the past century","volume":"9","author":"Oliver","year":"2018","journal-title":"Nature Commun."},{"key":"10.1016\/j.engappai.2023.107718_b41","first-page":"509","article-title":"Correlation between the Pacific Decadal Oscillation and East\/Japan Sea SST in the autumn","volume":"24","author":"Pak","year":"2019","journal-title":"Sea: J Korean Soc. Oceanogr."},{"key":"10.1016\/j.engappai.2023.107718_b42","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2022.946767","article-title":"Governing factors of the record-breaking marine heatwave over the mid-latitude western North Pacific in the summer of 2021","volume":"9","author":"Pak","year":"2022","journal-title":"Front. Mar. Sci."},{"key":"10.1016\/j.engappai.2023.107718_b43","doi-asserted-by":"crossref","first-page":"234","DOI":"10.7850\/jkso.2013.18.4.234","article-title":"An oceanic current map of the East Sea for science textbooks based on scientific knowledge acquired from oceanic measurements","volume":"18","author":"Park","year":"2013","journal-title":"Sea: J Korean Soc. Oceanogr."},{"key":"10.1016\/j.engappai.2023.107718_b44","series-title":"The \u201cmarine heat wave\u201d off Western Australia during the summer of 2010\/11","author":"Pearce","year":"2011"},{"key":"10.1016\/j.engappai.2023.107718_b45","series-title":"Improving Language Understanding by Generative Pre-Training","author":"Radford","year":"2018"},{"key":"10.1016\/j.engappai.2023.107718_b46","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecoinf.2016.10.004","article-title":"Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean","volume":"36","author":"Salles","year":"2016","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.engappai.2023.107718_b47","series-title":"Artificial Neural Networks and Machine Learning\u2013ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17\u201319, 2019, Proceedings, Part II 28","first-page":"714","article-title":"A study on catastrophic forgetting in deep LSTM networks","author":"Schak","year":"2019"},{"key":"10.1016\/j.engappai.2023.107718_b48","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.engappai.2023.107718_b49","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1038\/s41558-019-0412-1","article-title":"Marine heatwaves threaten global biodiversity and the provision of ecosystem services","volume":"9","author":"Smale","year":"2019","journal-title":"Nature Clim. Change"},{"key":"10.1016\/j.engappai.2023.107718_b50","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1016\/j.neucom.2009.11.030","article-title":"Multiple-output modeling for multi-step-ahead time series forecasting","volume":"73","author":"Taieb","year":"2010","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2023.107718_b51","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., 2015. Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 4489\u20134497.","DOI":"10.1109\/ICCV.2015.510"},{"key":"10.1016\/j.engappai.2023.107718_b52","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I., 2017. Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998\u20136008."},{"key":"10.1016\/j.engappai.2023.107718_b53","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L., 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 568\u2013578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.1016\/j.engappai.2023.107718_b54","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1126\/science.aad8745","article-title":"Climate-driven regime shift of a temperate marine ecosystem","volume":"353","author":"Wernberg","year":"2016","journal-title":"Science"},{"key":"10.1016\/j.engappai.2023.107718_b55","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/nclimate1627","article-title":"An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot","volume":"3","author":"Wernberg","year":"2013","journal-title":"Nature Clim. Change"},{"key":"10.1016\/j.engappai.2023.107718_b56","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.3390\/rs12101592","article-title":"Inter-comparisons of daily sea surface temperatures and in-situ temperatures in the coastal regions","volume":"12","author":"Woo","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2023.107718_b57","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., Zhang, L., 2021. Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 22\u201331.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"10.1016\/j.engappai.2023.107718_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2019.104502","article-title":"A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data","volume":"120","author":"Xiao","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.engappai.2023.107718_b59","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2019.2931728","article-title":"An adaptive scale sea surface temperature predicting method based on deep learning with attention mechanism","volume":"17","author":"Xie","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b60","doi-asserted-by":"crossref","first-page":"8583","DOI":"10.1002\/int.22957","article-title":"SelfMatch: Robust semisupervised time-series classification with self-distillation","volume":"37","author":"Xing","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.engappai.2023.107718_b61","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1080\/2150704X.2020.1746853","article-title":"Prediction of sea surface temperature using a multiscale deep combination neural network","volume":"11","author":"Xu","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b62","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/LGRS.2017.2780843","article-title":"A CFCC-LSTM model for sea surface temperature prediction","volume":"15","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2023.107718_b63","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1038\/nclimate3304","article-title":"Distinct global warming rates tied to multiple ocean surface temperature changes","volume":"7","author":"Yao","year":"2017","journal-title":"Nature Clim. Change"},{"key":"10.1016\/j.engappai.2023.107718_b64","doi-asserted-by":"crossref","first-page":"262","DOI":"10.3796\/KSFOT.2018.54.3.262","article-title":"Characteristics of egg and larval distributions and catch changes of anchovy in relation to abnormally high sea temperature in the South Sea of Korea","volume":"54","author":"Yoo","year":"2018","journal-title":"J. Korean Soc. Fish. Ocean Technol."},{"key":"10.1016\/j.engappai.2023.107718_b65","doi-asserted-by":"crossref","unstructured":"Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K., 2019. Be your own teacher: Improve the performance of convolutional neural networks via self distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3713\u20133722.","DOI":"10.1109\/ICCV.2019.00381"},{"key":"10.1016\/j.engappai.2023.107718_b66","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2017.2733548","article-title":"Prediction of sea surface temperature using long short-term memory","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197623019024?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197623019024?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T22:07:20Z","timestamp":1712614040000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197623019024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":66,"alternative-id":["S0952197623019024"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2023.107718","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Two-pathway spatiotemporal representation learning for extreme water temperature prediction","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2023.107718","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107718"}}