BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING
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Author/Creator ORCID
Date
2022
Type of Work
Department
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Citation of Original Publication
S. 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.
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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 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.
Subjects
Abstract
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.