BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING

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dc.contributor.authorAli, Sahara
dc.contributor.authorMostafa, Seraj Al Mahmud
dc.contributor.authorLi, Xingyan
dc.contributor.authorKhanjani, Sara
dc.contributor.authorWang, Jianwu
dc.contributor.authorFoulds, James
dc.contributor.authorJaneja, Vandana
dc.date.accessioned2022-09-26T14:45:20Z
dc.date.available2022-09-26T14:45:20Z
dc.date.issued2022
dc.descriptionIEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17 - 22 July 2022en
dc.description.abstractThe Arctic is a region with unique climate features, motivat- ing new AI methodologies to study it. Unfortunately, Arc- tic sea ice has seen a continuous decline since 1979. This not only poses a significant threat to Arctic wildlife and sur- rounding coastal communities but is also adversely affecting the global climate patterns. To study the potential of AI in tackling climate change, we analyze the performance of four probabilistic machine learning methods in forecasting sea-ice extent for lead times of up to 6 months, further comparing them with traditional machine learning methods. Our com- parative analysis shows that Gaussian Process Regression is a good fit to predict sea-ice extent for longer lead times with lowest RMSE error.en
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/9883505/en
dc.format.extent4 pagesen
dc.genreconference papers and proceedingsen
dc.genrepreprintsen
dc.genrecomputer codeen
dc.identifierdoi:10.13016/m2myuf-war4
dc.identifier.citationS. Ali et al., "Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice Forecasting," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 4654-4657, doi: 10.1109/IGARSS46834.2022.9883505.en
dc.identifier.urihttp://hdl.handle.net/11603/25878
dc.identifier.urihttps://doi.org/10.1109/IGARSS46834.2022.9883505
dc.language.isoenen
dc.publisherIEEEen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
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
dc.subjectUMBC Big Data Analytics Laben
dc.titleBENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTINGen
dc.typeTexten
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en
dcterms.creatorhttps://orcid.org/0000-0003-0130-6135en

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