ONE-WEEK WAVE PREDICTION USING GWM AND XGBOOST
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- Tracey H. A. TOM
- (株)ハイドロ総合技術研究所
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- MASE Hajime
- 京都大学
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- IKEMOTO Ai
- HOKUULA Corp.
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- KAWANAKA Ryuji
- (株)ハイドロ総合技術研究所
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- TAKEDA Masahide
- 東亜建設工業(株) 技術研究開発センター
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- HARA Chisato
- 東亜建設工業(株) 技術研究開発センター
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- KIM Sooyoul
- 熊本大学
Bibliographic Information
- Other Title
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- GWMとXGBoostを用いた１週間波浪予測
- GWM ト XGBoost オ モチイタ 1シュウカン ハロウ ヨソク
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Abstract
<p> The recent studies introduce machine learning-based wave prediction models using the Group Method of Data Handling (GMDH) and Artificial Neural Network (ANN) for one-week wave prediction along nearshore coasts in Japan because global wave forecast models predict waves on large spatial resolutions, being unreliable for nearshore wave predictions. The current study develops the GWM to XGBoost nearshore wave prediction model that transforms global wave model GWM forecast waves to nearshore ones. The XGBoost method is one of the machine learning techniques that the ensemble training method improves the discrimination efficiency by combining multiple-decision trees. Then, the study discused the accuracy of the GWM to XGBoost nearshore wave prediction model, and showed a good performance.</p>
Journal
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- Journal of Japan Society of Civil Engineers, Ser. B3 (Ocean Engineering)
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Journal of Japan Society of Civil Engineers, Ser. B3 (Ocean Engineering) 77 (2), I_7-I_12, 2021
Japan Society of Civil Engineers