Толқынның елеулі биіктігін болжауға арналған машиналық оқыту негізіндегі тәсіл
DOI:
https://doi.org/10.26577/JMMCS.2021.v110.i2.08Кілттік сөздер:
Machine learning, significant wave height, Support vector regressionАннотация
Мұхит толқынының елеулi биiктiгiн бағалауға арналған машиналық оқыту жүйесi құрылды. Толқынның елеулi биiктiгi мен толқын периоды мұхит жағдайларын болжау үшiн бағыланатын машиналық оқыту барысында пайдаланылуы мүмкiн. Бұл жұмыста тiрек векторы әдiсi негiзiндегi регрессия көмегiмен (Support vector regression – SVR) толқынныц елеулi биiктiгiн болжау әдiсi ұсынылды. Буй деректер жиыны Квинсленд өкiметiнiц ашық деректер порталынан алынды, кiрiс деректерi бағыланатын оқыту мен тестiлеу үшiн деректер жиынтығына бiрiктiрiлдi. SVR моделi толқынныц елеулi биiктiгiн 0,044 орташа квадратты қателiкпен көрсеттi және тестiлеу деректерiнде 95% дәлдiкпен бойынша орындалды. Толқынныц елеулi биiктiгiн физикалы модель негiзiнде болжаумен салыстырғанда, машиналық оқыту негiзiндегi SVR моделi айтарлықтай аз есептеу уақытын (< 1/1200) қажет етедi.
Тyйiн сbздер: Машиналық оқыту, толқынныц елеулi биiктiгi, тiрек векторы әдiсi негiзiндегi регрессия.
Библиографиялық сілтемелер
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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.
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[25] Vapnik, V. The Nature of Statistical Learning Theory (Springer, New York, 1995).
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Как цитировать
Ahmad, Z., & Mansurova, M. (2021). Толқынның елеулі биіктігін болжауға арналған машиналық оқыту негізіндегі тәсіл. Қазұу Хабаршысы. Математика, механика, информатика сериясы, 110(2), 87–96. https://doi.org/10.26577/JMMCS.2021.v110.i2.08
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