A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland
1
Department of Geography, University of Sheffield, Sheffield S10 2TN, UK
2
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
3
School of Geography and Lincoln Centre for Water and Planetary Health, University of Lincoln, Lincoln LN6 7FL, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Paul Tae-Woo Lee
Sustainability 2021, 13(14), 7705; https://doi.org/10.3390/su13147705
Received: 25 May 2021 / Revised: 23 June 2021 / Accepted: 7 July 2021 / Published: 9 July 2021
(This article belongs to the Special Issue Modelling for Sustainable Marine Management)
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.
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Keywords:
icebergs; modeling; prediction; Canada
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MDPI and ACS Style
Ross, J.B.; Bigg, G.R.; Zhao, Y.; Hanna, E. A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland. Sustainability 2021, 13, 7705. https://doi.org/10.3390/su13147705
AMA Style
Ross JB, Bigg GR, Zhao Y, Hanna E. A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland. Sustainability. 2021; 13(14):7705. https://doi.org/10.3390/su13147705
Chicago/Turabian StyleRoss, Jennifer B., Grant R. Bigg, Yifan Zhao, and Edward Hanna. 2021. "A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland" Sustainability 13, no. 14: 7705. https://doi.org/10.3390/su13147705
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