Подход на основе машинного обучения для прогнозирования значительной высоты волны
DOI:
https://doi.org/10.26577/JMMCS.2021.v110.i2.08Ключевые слова:
Machine learning, significant wave height, Support vector regressionАннотация
Разработана система машинного обучения для оценки значительной высоты океанской волны. Значительная высота и период волны могут быть использованы при контролируемом машинном обучении для прогнозирования состояния океана. В данной работе предложен метод для прогнозирования значительной высоты волны с помощью регрессии на основе метода опорных векторов (Support vector regression – SVR). Набор данных буев взят с портала открытых данных правительства Квинсленда, входные данные с которого были объединены в наборы данных для контролируемого обучения и тестирования.
Модель SVR воспроизводила значительную высоту волны со среднеквадратической ошибкой 0,044 и выполнялась на тестовых данных с точностью 95%. По сравнению с прогнозированием значительной высоты волны на основе физической модели, для модели SVR с машинным обучением требуется значительно меньше (< 1/1200) времени вычислений.
Ключевые слова: Машинное обучение, значимая высота волны, регрессия на основе метода опорных векторов.
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[7] V. Mallet, G. Stoltz, B. Mauricette, "Ozone ensemble forecast with machine learning algorithms" , Journal of Geophysical Research: Atmospheres 114 (2009).
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[10] A. Etemad-Shahidi, J. Mahjoobi, "Comparison between M5’ model tree and neural networks for prediction of significant wave height in lake superior" , Ocean Engineering 36 (2009): 1175-1181.
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[17] Y. Song, D. Haidvogel, "A semi-implicit ocean circulation model using a generalized topography-following coordinate system" , Journal of Computational Physics 115 (1994): 228-244.
[18] J. Patterson, J. Thomas, L. Rosenfeld, J. Newton, L. Hazard, J. Scianna, R. Kudela, E. Mayorga, C. Cohen, M. Cook, et al., "Addressing ocean and coastal issues at the west coast scale through regional ocean observing system collaboration,
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2825-2830.
[22] Scott C James, Yushan Zhang, Fearghal O’Donncha, "See discussions, A Machine Learning Framework to Forecast Wave Conditions" , Coastal Engineering (2017): 46556-5637.
[23] N. Cesa-Bianchi, A. Conconi, C. Gentile, "On the generalization ability of on-line learning algorithms" , IEEE Transactions on Information Theory 50 (2004): 2050-2057.
[24] Queensland Government Coastal Data System - Near real time wave data, open data portal https://www.data.qld.gov.au/dataset/coastal-data-system-near-real-time-wave-data
[25] Vapnik, V. The Nature of Statistical Learning Theory (Springer, New York, 1995).
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Как цитировать
Ahmad, Z., & Mansurova, M. (2021). Подход на основе машинного обучения для прогнозирования значительной высоты волны. Вестник КазНУ. Серия математика, механика, информатика, 110(2), 87–96. https://doi.org/10.26577/JMMCS.2021.v110.i2.08
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Информатика
