{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T17:23:42Z","timestamp":1776533022898,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T00:00:00Z","timestamp":1557100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we applied an artificial neural network (ANN) to the short-term prediction of the Arctic sea ice concentration (SIC). The prediction was performed using encoding and decoding processes, in which a gated recurrent unit encodes sequential sea ice data, and a feed-forward neural network model decodes the encoded input data. Because of the large volume of Arctic sea ice data, the ANN predicts the future SIC of each cell individually. The limitation of these singular predictions is that they do not use information from other cells. This results in low accuracy, particularly when there are drastic changes during melting and freezing seasons. To address this issue, we present a new data scheme including global and local SIC information, where the global information is represented by sea ice statistics. We trained ANNs using different data schemes and network architectures, and then compared their performances quantitatively and visually. The results show that, compared with a data scheme that uses only local sea ice information, the newly proposed scheme leads to a significant improvement in prediction accuracy.<\/jats:p>","DOI":"10.3390\/rs11091071","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T08:19:59Z","timestamp":1557389999000},"page":"1071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-0210","authenticated-orcid":false,"given":"Minjoo","family":"Choi","sequence":"first","affiliation":[{"name":"Mathematics and Cybernetics, SINTEF Digital, P.O. Box 124 Blindern, NO-0314 Oslo, Norway"}]},{"given":"Liyanarachchi Waruna Arampath","family":"De Silva","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8561, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0849-9689","authenticated-orcid":false,"given":"Hajime","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8561, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,6]]},"reference":[{"key":"ref_1","unstructured":"Kitagawa, H. (1996). Can the Northern Sea Route be Profitable?. Northern Sea Route, Future and Perspective, Proceedings of the INSROP Symposium Tokyo, \u201895, Tokyo, 1\u20136 October 1995, Ship and Ocean Foundation."},{"key":"ref_2","first-page":"18","article-title":"Ice-ocean Coupled Computations for Sea-ice Prediction to Support Ice Navigation in Arctic Sea Routes","volume":"34","author":"Yamaguchi","year":"2015","journal-title":"Polar Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1175\/JCLI3712.1","article-title":"The New Hadley Centre Climate Model (HadGEM1): Evaluation of Coupled Simulations","volume":"19","author":"Johns","year":"2006","journal-title":"J. Climate."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1175\/BAMS-D-13-00255.1","article-title":"The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability","volume":"96","author":"Kay","year":"2015","journal-title":"Bull. Amer. Meteor. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6312","DOI":"10.1175\/2010JCLI3679.1","article-title":"Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity","volume":"23","author":"Watanabe","year":"2010","journal-title":"J. Climate."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20193","DOI":"10.3402\/polar.v32i0.20193","article-title":"Influence of Winter Sea-ice Motion on Summer Ice Cover in the Arctic","volume":"32","author":"Kimura","year":"2013","journal-title":"Polar Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1175\/MWR-D-12-00057.1","article-title":"Seasonal Prediction of Arctic Sea Ice Extent from a Coupled Dynamical Forecast System","volume":"141","author":"Wang","year":"2013","journal-title":"Month. Weather Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"L0850","DOI":"10.1029\/2008GL033244","article-title":"Ensemble 1-Year Predictions of Arctic Sea Ice for the Spring and Summer of 2008","volume":"35","author":"Zhang","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8327","DOI":"10.1002\/2015JC011283","article-title":"Short-term Sea Ice Forecasting: An Assessment of Ice Concentration and Ice Drift Forecasts using the U.S. Navy\u2019s Arctic Cap Nowcast\/Forecast System","volume":"120","author":"Hebert","year":"2015","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"633","DOI":"10.5194\/os-8-633-2012","article-title":"TOPAZ4: An Ocean-sea Ice Data Assimilation System for the North Atlantic and Arctic","volume":"8","author":"Sakov","year":"2012","journal-title":"Ocean Sci."},{"key":"ref_11","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Stateline, NV, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","article-title":"Context-dependent Pre-trained Deep Neural Networks for Large-vocabulary Speech Recognition","volume":"20","author":"Dahl","year":"2012","journal-title":"IEEE Transact. Audio Speech Lang Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2017, January 21\u201326). Photo-realistic Single Image Super-resolution using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_14","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.3390\/rs9121305","article-title":"Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network","volume":"9","author":"Chi","year":"2017","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comp."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, K., Cho, J., Kang, Y., Yoon, H.J., and Lee, Y.W. (2019). Satellite-based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-model Ensemble. Remote Sens., 11.","DOI":"10.3390\/rs11010019"},{"key":"ref_18","first-page":"382","article-title":"Bayesian Model Averaging: A Tutorial","volume":"14","author":"Hoeting","year":"1999","journal-title":"Stat. Sci."},{"key":"ref_19","unstructured":"Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H.J. (1996). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM\/I-SSMIS Passive Microwave Data, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center."},{"key":"ref_20","unstructured":"Cavalieri, D.J., Markus, T., and Comiso, J.C. (2014). AMSR-E\/Aqua Daily L3 12.5 km Brightness Temperature, Sea Ice Concentration, & Snow Depth Polar Grids, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center."},{"key":"ref_21","unstructured":"Hori, M., Yabuki, H., Sugimura, T., and Terui, T. (2019, April 19). AMSR2 Level 3 product of Daily Polar Brightness Temperatures and Product, Version 1.00, Japan, Arctic Data archive System (ADS), 2012. Available online: https:\/\/ads.nipr.ac.jp\/portal\/kiwa\/Summary.action?owner_site=ADS&selectFile=A20170123-003&version=1.00&scr=list_home."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-decoder for Statistical Machine Translation. arXiv preprint.","DOI":"10.3115\/v1\/D14-1179"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1071\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:49:32Z","timestamp":1760186972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,6]]},"references-count":22,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091071"],"URL":"https:\/\/doi.org\/10.3390\/rs11091071","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,6]]}}}