Multi-Task Spatiotemporal Deep Learning Based Arctic Sea Ice Prediction
| dc.contributor.author | Bourne Jr., Jamal | |
| dc.contributor.author | Hu, Michael | |
| dc.contributor.author | Kim, Eliot | |
| dc.contributor.author | Kruse, Peter | |
| dc.contributor.author | Lama, Skylar | |
| dc.contributor.author | Ali, Sahara | |
| dc.contributor.author | Huang, Yiyi | |
| dc.contributor.author | Wang, Jianwu | |
| dc.date.accessioned | 2021-11-05T17:47:43Z | |
| dc.date.available | 2021-11-05T17:47:43Z | |
| dc.date.issued | 2021 | |
| dc.description.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. | en_US |
| dc.description.sponsorship | This work is supported by the grant “REU Site: Online Interdisciplinary Big Data Analytics in Science and Engineering” from the National Science Foundation (grant no. OAC–2050943). Co-author Ali and Wang additionally acknowledge support by the grant “CAREER: Big Data Climate Causality Analytics” from the National Science Foundation (grant no. OAC–1942714). The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/BigDataREU2021Team1.pdf | en_US |
| dc.format.extent | 20 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2tk7h-gil8 | |
| dc.identifier.citation | 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 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/23253 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UMBC HPCF | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartofseries | HPCF;2021–11 | |
| dc.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. | en_US |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.title | Multi-Task Spatiotemporal Deep Learning Based Arctic Sea Ice Prediction | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-9933-1170 | en_US |