Multi-Task Spatiotemporal Deep Learning Based Arctic Sea Ice Prediction
Loading...
Permanent Link
Author/Creator ORCID
Date
2021
Type of Work
Department
Program
Citation of Original Publication
Bourne Jr., Jamal et al.; Multi-Task Spatiotemporal Deep Learning Based Arctic Sea Ice Prediction; UMBC High Performance Computing Facilty (HPCF), 2021; http://hpcf-files.umbc.edu/research/papers/BigDataREU2021Team1.pdf
Rights
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.
Abstract
Important natural resources in the Arctic rely heavily on sea ice, making it important to
forecast Arctic sea ice changes. Arctic sea ice forecasting often involves two connected tasks: sea
ice concentration at each pixel and overall sea ice extent. Instead of having two separate models
for two forecasting tasks, in this report, we study how to use multi-task learning techniques
and leverage the connections between ice concentration and ice extent to improve accuracy for
both prediction tasks. Because of the spatiotemporal nature of the data, we designed two novel
multi-task learning models based on CNNs and ConvLSTMs, respectively. We also developed a
custom loss function which trains the models to ignore land pixels when making predictions. Our
experiments show our models can have better accuracies than separate models that predict sea
ice extent and concentration separately, and that our accuracies are better than or comparable
with results in the state-of-the-art studies.