Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary

Luo Yao; Shi Hui; Zhang ZhenqiuZhang ChengZhou WeiPan GangWang Weiqiang

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Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary

机译：Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary

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摘要

In this study, a novel data-driven model is proposed for efficient spatial-temporal forecasting of the significant wave height (SWH), mean wave period (MWP), and mean wave direction (MWD) in the Pearl River Estuary, located in Southern China, using machine learning. The model utilizes machine learning techniques and employs a multi-layer perceptron (MLP) and decision tree (DT) based architecture following a matrix-to -matrix mapping strategy to establish the underlying spatial-temporal relationship between the wind and wave fields for four-day and ocean-scale forecasts. The training and testing of the model were carried out using long-term hindcast wave and wind datasets released by ERA5. A rectangular sea region covering an area of 5 degrees x 5 degrees in the Pearl River Estuary was regarded as the modeling domain. The data-driven model was validated against a 10 years of historical hindcast data, and the global errors were analyzed. The forecasting performance of the established model for tropical cyclone wave events was investigated in terms of both the spatial pattern and temporal variance of the SWH, MWP, and MWD. Extreme waves were generated using the proposed model and compared with the original hindcast results. The data-driven model showed promising potential for accurately capturing ambiguous patterns and features that are variant in both spatial and temporal dimensions, and exhibited obvious superiority over numerical wave modeling in terms of computational efficiency.

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来源
《Ocean engineering》 |2023年第1期|1.1-1.20|共20页
作者
Luo Yao; Shi Hui; Zhang ZhenqiuZhang ChengZhou WeiPan GangWang Weiqiang;
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作者单位

Chinese Acad Sci;

Sun Yat Sen Univ;

Univ DelawareChina Water Resources Pearl River Planning Surveyi;

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原文格式 PDF
正文语种英语
中图分类海洋学;
关键词
Wave forecast; Machine learning; MLP; Decision trees; Cross validation;

Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary

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