BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ...
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17 - 22 July 2022 ... : The 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...
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IEEE
2022
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| Online Access: | https://dx.doi.org/10.13016/m2myuf-war4 https://mdsoar.org/handle/11603/25878 |
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ftdatacite:10.13016/m2myuf-war4 2023-08-27T04:07:18+02:00 BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... Ali, Sahara Mostafa, Seraj Al Mahmud Li, Xingyan Khanjani, Sara Wang, Jianwu Foulds, James Janeja, Vandana 2022 https://dx.doi.org/10.13016/m2myuf-war4 https://mdsoar.org/handle/11603/25878 en eng IEEE This 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. This 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. UMBC Big Data Analytics Lab Text Collection article 2022 ftdatacite https://doi.org/10.13016/m2myuf-war4 2023-08-07T14:24:23Z IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17 - 22 July 2022 ... : The 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. ... Article in Journal/Newspaper Arctic Climate change Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
| language |
English |
| topic |
UMBC Big Data Analytics Lab |
| spellingShingle |
UMBC Big Data Analytics Lab Ali, Sahara Mostafa, Seraj Al Mahmud Li, Xingyan Khanjani, Sara Wang, Jianwu Foulds, James Janeja, Vandana BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... |
| topic_facet |
UMBC Big Data Analytics Lab |
| description |
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17 - 22 July 2022 ... : The 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. ... |
| format |
Article in Journal/Newspaper |
| author |
Ali, Sahara Mostafa, Seraj Al Mahmud Li, Xingyan Khanjani, Sara Wang, Jianwu Foulds, James Janeja, Vandana |
| author_facet |
Ali, Sahara Mostafa, Seraj Al Mahmud Li, Xingyan Khanjani, Sara Wang, Jianwu Foulds, James Janeja, Vandana |
| author_sort |
Ali, Sahara |
| title |
BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... |
| title_short |
BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... |
| title_full |
BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... |
| title_fullStr |
BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... |
| title_full_unstemmed |
BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING ... |
| title_sort |
benchmarking probabilistic machine learning models for arctic sea ice forecasting ... |
| publisher |
IEEE |
| publishDate |
2022 |
| url |
https://dx.doi.org/10.13016/m2myuf-war4 https://mdsoar.org/handle/11603/25878 |
| geographic |
Arctic |
| geographic_facet |
Arctic |
| genre |
Arctic Climate change Sea ice |
| genre_facet |
Arctic Climate change Sea ice |
| op_rights |
This 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. This 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. |
| op_doi |
https://doi.org/10.13016/m2myuf-war4 |
| _version_ |
1775348097234239488 |