BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTIC SEA ICE FORECASTING

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2022 Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice (1).pdf (506.42 KB)
 sea-ice-prediction-main.zip (4.97 MB)

Links to Files

https://ieeexplore.ieee.org/abstract/document/9883505/

Permanent Link

http://hdl.handle.net/11603/25878
 https://doi.org/10.1109/IGARSS46834.2022.9883505

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UMBC Information Systems Department
iHARP: NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-9933-1170
 https://orcid.org/0000-0003-0130-6135

Date

2022

Type of Work

conference papers and proceedings
preprints
computer code
Text

Department

Program

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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Subjects

UMBC Big Data Analytics Lab

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.