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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2021
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ftdoajarticles:oai:doaj.org/article:84c10aa0ffc045718f0a38d39d92e6c3 2023-05-15T17:22:31+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 2021-07-01T00:00:00Z https://doi.org/10.3390/su13147705 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 EN eng MDPI AG https://www.mdpi.com/2071-1050/13/14/7705 https://doaj.org/toc/2071-1050 doi:10.3390/su13147705 2071-1050 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 Sustainability, Vol 13, Iss 7705, p 7705 (2021) icebergs modeling prediction Canada Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 article 2021 ftdoajarticles https://doi.org/10.3390/su13147705 2022-12-31T04:04:29Z 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 Directory of Open Access Journals: DOAJ Articles Canada Sustainability 13 14 7705 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
icebergs modeling prediction Canada Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
spellingShingle |
icebergs modeling prediction Canada Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 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 Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
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 |
https://www.mdpi.com/2071-1050/13/14/7705 https://doaj.org/toc/2071-1050 doi:10.3390/su13147705 2071-1050 https://doaj.org/article/84c10aa0ffc045718f0a38d39d92e6c3 |
op_doi |
https://doi.org/10.3390/su13147705 |
container_title |
Sustainability |
container_volume |
13 |
container_issue |
14 |
container_start_page |
7705 |
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1766109239419338752 |