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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Bibliographic Details
Main Authors: Ali, Sahara, Mostafa, Seraj Al Mahmud, Li, Xingyan, Khanjani, Sara, Wang, Jianwu, Foulds, James, Janeja, Vandana
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2022
Subjects:
UMBC Big Data Analytics Lab
Arctic
Climate change
Sea ice
Online Access:https://dx.doi.org/10.13016/m2myuf-war4
https://mdsoar.org/handle/11603/25878
Description
Summary: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. ...

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