A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland
Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategi...
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MDPI AG
2021
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Online Access: | https://doi.org/10.3390/su13147705 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:84c10aa0ffc045718f0a38d39d92e6c3 2023-05-15T17:22:28+02:00 A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland Jennifer B. Ross Grant R. Bigg Yifan Zhao Edward Hanna 2021-07-01 https://doi.org/10.3390/su13147705 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 en eng MDPI AG doi:10.3390/su13147705 2071-1050 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 undefined Sustainability, Vol 13, Iss 7705, p 7705 (2021) icebergs modeling prediction Canada geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.3390/su13147705 2023-01-22T19:12:44Z Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year. Article in Journal/Newspaper Newfoundland North Atlantic Unknown Canada Sustainability 13 14 7705 |
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Open Polar |
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Unknown |
op_collection_id |
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language |
English |
topic |
icebergs modeling prediction Canada geo envir |
spellingShingle |
icebergs modeling prediction Canada geo envir Jennifer B. Ross Grant R. Bigg Yifan Zhao Edward Hanna A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland |
topic_facet |
icebergs modeling prediction Canada geo envir |
description |
Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year. |
format |
Article in Journal/Newspaper |
author |
Jennifer B. Ross Grant R. Bigg Yifan Zhao Edward Hanna |
author_facet |
Jennifer B. Ross Grant R. Bigg Yifan Zhao Edward Hanna |
author_sort |
Jennifer B. Ross |
title |
A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland |
title_short |
A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland |
title_full |
A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland |
title_fullStr |
A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland |
title_full_unstemmed |
A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland |
title_sort |
combined control systems and machine learning approach to forecasting iceberg flux off newfoundland |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/su13147705 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland North Atlantic |
genre_facet |
Newfoundland North Atlantic |
op_source |
Sustainability, Vol 13, Iss 7705, p 7705 (2021) |
op_relation |
doi:10.3390/su13147705 2071-1050 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 |
op_rights |
undefined |
op_doi |
https://doi.org/10.3390/su13147705 |
container_title |
Sustainability |
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13 |
container_issue |
14 |
container_start_page |
7705 |
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1766109162506289152 |