Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ...
The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensem...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2312.04330 https://arxiv.org/abs/2312.04330 |
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ftdatacite:10.48550/arxiv.2312.04330 2024-01-28T10:03:47+01:00 Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... Borisova, Julia Nikitin, Nikolay O. 2023 https://dx.doi.org/10.48550/arxiv.2312.04330 https://arxiv.org/abs/2312.04330 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Machine Learning cs.LG Artificial Intelligence cs.AI Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences Article Preprint CreativeWork article 2023 ftdatacite https://doi.org/10.48550/arxiv.2312.04330 2024-01-04T21:32:41Z The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea. ... : 7 pages, 6 figures ... Article in Journal/Newspaper Arctic Kara Sea Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic Kara Sea |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Machine Learning cs.LG Artificial Intelligence cs.AI Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
spellingShingle |
Machine Learning cs.LG Artificial Intelligence cs.AI Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences Borisova, Julia Nikitin, Nikolay O. Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... |
topic_facet |
Machine Learning cs.LG Artificial Intelligence cs.AI Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
description |
The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea. ... : 7 pages, 6 figures ... |
format |
Article in Journal/Newspaper |
author |
Borisova, Julia Nikitin, Nikolay O. |
author_facet |
Borisova, Julia Nikitin, Nikolay O. |
author_sort |
Borisova, Julia |
title |
Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... |
title_short |
Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... |
title_full |
Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... |
title_fullStr |
Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... |
title_full_unstemmed |
Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble ... |
title_sort |
surrogate modelling for sea ice concentration using lightweight neural ensemble ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2312.04330 https://arxiv.org/abs/2312.04330 |
geographic |
Arctic Kara Sea |
geographic_facet |
Arctic Kara Sea |
genre |
Arctic Kara Sea Sea ice |
genre_facet |
Arctic Kara Sea Sea ice |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2312.04330 |
_version_ |
1789329256293072896 |