{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T01:41:06Z","timestamp":1780537266043,"version":"3.54.1"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["42301478"],"award-info":[{"award-number":["42301478"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42275028"],"award-info":[{"award-number":["42275028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Foundation of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["22KJB170016"],"award-info":[{"award-number":["22KJB170016"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3411109","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T17:31:47Z","timestamp":1717781507000},"page":"81772-81782","source":"Crossref","is-referenced-by-count":8,"title":["Evaluation of Different Deep Learning Methods for Meteorological Element Forecasting"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3120-066X","authenticated-orcid":false,"given":"Ruibo","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-644X","authenticated-orcid":false,"given":"Wen","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guojie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zicong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Zurich, Z&#x00FC;rich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1175\/2009JCLI2647.1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-11283-w"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1029\/2022GL097726"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s43016-021-00335-4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/qj.49703916612"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2005.1676"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2014.07.012"},{"issue":"6","key":"ref8","first-page":"12","article-title":"Comparative study of ANN, ANFIS and AR model for daily runoff time series prediction","volume":"14","author":"Tan","year":"2016","journal-title":"South-North Water Transfers Water Sci. Technol."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/EEM.2017.7982035"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1177\/0013164407310131"},{"key":"ref11","article-title":"Relative humidity and mean monthly temperature forecasts in Ahwaz Station with ARIMA model in time series analysis","volume-title":"Proc. Int. Conf. Environ. Ind. Innov. (IPCBEE)","volume":"12","author":"Sarraf"},{"key":"ref12","article-title":"Comparison between ARIMA and deep learning models for temperature forecasting","author":"De Saa","year":"2020","journal-title":"arXiv:2011.04452"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482315"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-012-0157-3"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2017.02.011"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2012.07.041"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1175\/mwr2906.1"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC.2010.5584337"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-013-0867-3"},{"issue":"1","key":"ref20","first-page":"65","article-title":"Facial expression recognition based on deep learning and traditional machine learning","volume":"45","author":"Wang","year":"2018","journal-title":"Appl. Sci. Technol."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1126\/science.1197962"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.01.177"},{"key":"ref24","first-page":"1","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Shi"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2022.127653"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2018.11.044"},{"key":"ref27","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref28","article-title":"Graph learning approaches to recommender systems: A review","author":"Wang","year":"2020","journal-title":"arXiv:2004.11718"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3659"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-021-0229-5"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3505244"},{"key":"ref36","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2478\/jaiscr-2019-0006"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-03282-z"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"ref41","first-page":"7154","article-title":"DAG-GNN: DAG structure learning with graph neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","volume":"97","author":"Yu"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref44","first-page":"22419","article-title":"AutoFormer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-016-3400-4"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1002\/2016RG000550"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1007\/978-1-4612-2804-2_20","volume-title":"Landscape Boundaries: Consequences for Biotic Diversity and Ecological Flows","author":"Fu","year":"1992"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.2134\/agronj2005.0126"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.agee.2014.01.030"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"ref51","first-page":"2261","article-title":"Evaluation and comparison of downward solar radiation from new generation atmospheric reanalysis ERA5 across Mainland China","volume":"23","author":"Zhang","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1002\/joc.7166"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-016-3302-5"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.05.074"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/c2020-0-01677-4"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206906"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2017.2769220"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0209"},{"key":"ref60","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv:1803.01271"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.21437\/SSW.2016"},{"key":"ref62","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref64","volume-title":"R-NET: Machine reading comprehension with self-matching networks","year":"2017"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1917285117"},{"key":"ref66","article-title":"FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators","author":"Pathak","year":"2022","journal-title":"arXiv:2202.11214"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1559-7"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10551823.pdf?arnumber=10551823","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T21:02:50Z","timestamp":1719349370000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10551823\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3411109","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}