Journal of Marine Systems, volume 186, pages 29-36
An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts
Publication type: Journal Article
Publication date: 2018-10-01
Journal:
Journal of Marine Systems
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 2.8
ISSN: 09247963, 18791573
Ecology, Evolution, Behavior and Systematics
Aquatic Science
Oceanography
Abstract
This study investigates near-shore circulation and wave characteristics applied to a case-study site in Monterey Bay, California. We integrate physics-based models to resolve wave conditions together with a machine-learning algorithm that combines forecasts from multiple, independent models into a single “best-estimate” prediction of the true state. The Simulating WAves Nearshore (SWAN) physics-based model is used to compute wind-augmented waves. Ensembles are developed based on multiple simulations perturbing data input to the model. A learning-aggregation technique uses historical observations and model forecasts to calculate a weight for each ensemble member. We compare the weighted ensemble predictions with measured data to evaluate performance against present state-of-the-art. Finally, we discuss how this framework that integrates data-driven and physics-based approaches can outperform either technique in isolation.
Citations by journals
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5
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Applied Ocean Research
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Applied Ocean Research
5 publications, 13.89%
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Ocean Engineering
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5 publications, 13.89%
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Journal of Marine Systems
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Journal of Marine Systems
2 publications, 5.56%
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Computers and Geosciences
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Computers and Geosciences
1 publication, 2.78%
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Procedia Computer Science
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Procedia Computer Science
1 publication, 2.78%
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Geophysical Research Letters
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Geophysical Research Letters
1 publication, 2.78%
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Frontiers in Earth Science
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Frontiers in Earth Science
1 publication, 2.78%
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Applied Sciences (Switzerland)
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Applied Sciences (Switzerland)
1 publication, 2.78%
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Fluids
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Fluids
1 publication, 2.78%
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Remote Sensing
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Remote Sensing
1 publication, 2.78%
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Frontiers in Marine Science
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Frontiers in Marine Science
1 publication, 2.78%
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Journal of Earth System Science
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Journal of Earth System Science
1 publication, 2.78%
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Natural Hazards
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Natural Hazards
1 publication, 2.78%
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Water Resources Management
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Water Resources Management
1 publication, 2.78%
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Nature Reviews Physics
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Nature Reviews Physics
1 publication, 2.78%
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Ecological Informatics
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Ecological Informatics
1 publication, 2.78%
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Ocean Modelling
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Ocean Modelling
1 publication, 2.78%
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Marine Structures
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Marine Structures
1 publication, 2.78%
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Journal of Computational Physics
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Journal of Computational Physics
1 publication, 2.78%
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Environmental Modelling and Software
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Environmental Modelling and Software
1 publication, 2.78%
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Coastal Engineering
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Coastal Engineering
1 publication, 2.78%
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Quarterly Journal of the Royal Meteorological Society
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Quarterly Journal of the Royal Meteorological Society
1 publication, 2.78%
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Lecture Notes in Computer Science
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Lecture Notes in Computer Science
1 publication, 2.78%
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Energies
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Energies
1 publication, 2.78%
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Earth and Space Science
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Earth and Space Science
1 publication, 2.78%
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Journal of Fluid Mechanics
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Journal of Fluid Mechanics
1 publication, 2.78%
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3
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5
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Citations by publishers
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5
10
15
20
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Elsevier
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Elsevier
20 publications, 55.56%
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Springer Nature
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Springer Nature
5 publications, 13.89%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
4 publications, 11.11%
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Wiley
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Wiley
3 publications, 8.33%
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Frontiers Media S.A.
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Frontiers Media S.A.
2 publications, 5.56%
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Cambridge University Press
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Cambridge University Press
1 publication, 2.78%
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5
10
15
20
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Odonncha F. et al. An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts // Journal of Marine Systems. 2018. Vol. 186. pp. 29-36.
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Odonncha F., Zhang Y., Chen B., James S. C. An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts // Journal of Marine Systems. 2018. Vol. 186. pp. 29-36.
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TY - JOUR
DO - 10.1016/j.jmarsys.2018.05.006
UR - https://doi.org/10.1016%2Fj.jmarsys.2018.05.006
TI - An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts
T2 - Journal of Marine Systems
AU - Odonncha, Fearghal
AU - Zhang, Yushan
AU - Chen, Bei
AU - James, Scott C.
PY - 2018
DA - 2018/10/01 00:00:00
PB - Elsevier
SP - 29-36
VL - 186
SN - 0924-7963
SN - 1879-1573
ER -
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@article{2018_Odonncha,
author = {Fearghal Odonncha and Yushan Zhang and Bei Chen and Scott C. James},
title = {An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts},
journal = {Journal of Marine Systems},
year = {2018},
volume = {186},
publisher = {Elsevier},
month = {oct},
url = {https://doi.org/10.1016%2Fj.jmarsys.2018.05.006},
pages = {29--36},
doi = {10.1016/j.jmarsys.2018.05.006}
}