Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM (Papers Track)

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dc.contributor.authorAli, Sahara
dc.contributor.authorHuang, Yiyi
dc.contributor.authorHuang, Xin
dc.contributor.authorWang, Jianwu
dc.date.accessioned2022-09-26T15:54:48Z
dc.date.available2022-09-26T15:54:48Z
dc.descriptionICML 2021 Workshop Tackling Climate Change with Machine Learning
dc.description.abstractAccurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife.en_US
dc.description.urihttps://www.climatechange.ai/papers/icml2021/50en_US
dc.genreconference papers and proceedingsen_US
dc.genrepresentations (communicative events)en_US
dc.genrevideo recordingsen_US
dc.identifierdoi:10.13016/m2blgg-blec
dc.identifier.citation"Ali, Sahara et al. Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM. ICML 2021 Workshop Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/icml2021/50."en_US
dc.identifier.urihttp://hdl.handle.net/11603/25884
dc.language.isoen_USen_US
dc.publisherClimate Change AIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.subjectdecline in sea iceen_US
dc.subjectArctic sea ice forecastingen_US
dc.subjectattention-based Longen_US
dc.subjectShort Term Memory (LSTM) ensemble methoden_US
dc.subjectUMBC Big Data Analytics Lab
dc.titleForecasting Sea Ice Concentrations using Attention-based Ensemble LSTM (Papers Track)en_US
dc.typeMoving Imageen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

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