基于观测数据的南海波浪分析及预测模型对比

岳菁鹏; 梁晓兵; 潘荣才; 李扬; 侯二虎

doi:10.19912/j.0254-0096.tynxb.2025-0709

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基于观测数据的南海波浪分析及预测模型对比

更新时间：2026-10-15

- 基于观测数据的南海波浪分析及预测模型对比
- Vol. 47, Issue 9, Pages: 10(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0709
  CLC： P743
- Online First：10 October 2026，
  
  Published：2026
- 稿件说明：
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岳菁鹏, 梁晓兵, 潘荣才, et al. 基于观测数据的南海波浪分析及预测模型对比[J]. 2026, 47(9): 10.
DOI：

岳菁鹏, 梁晓兵, 潘荣才, et al. 基于观测数据的南海波浪分析及预测模型对比[J]. 2026, 47(9): 10. DOI： 10.19912/j.0254-0096.tynxb.2025-0709.

摘要

选取分别位于南海北部、中部与南部海域的4个观测站

对各站实测波浪数据的特征值、联合分布及季度特性等进行对比分析。利用深度学习模型对各站位的波浪预测效果进行研究

分析隐藏层数、优化算法和模型类型3个变量对预测性能的影响。研究结果表明

各海域处的波高及波周期均主要集中在0~4 m及4~9 s区间

北部海域的波高及波周期分布范围相对集中

中部及南部海域的分布范围更广

靠近大陆的近岸海域的波浪能资源明显偏小

中部及北部海域的波高在秋、冬季明显大于春、夏季;门控循环单元（GRU）相较于长短期记忆网络（LSTM）和多层感知机（MLP）模型表现出更优的预测能力

预测模型的隐藏层数为1时

并采用Adam和RMSProp优化算法时的误差更小。

Abstract

This study selects four observation stations in the northern

central

and southern areas of the South China Sea to conduct comparative analyses of measured wave data characteristics

joint distributions

and seasonal variations. Deep learning models were employed to investigate wave prediction performance

with emphasis on the impacts of hidden layer quantity

optimization algorithms

and model architecture. Results demonstrate that wave heights and periods predominantly concentrate in 0-4 m and 4-9 s ranges across all regions. Waves in the northem area of the South China Sea waves display narrower parameter distributions compared to broader ranges in central and southern zones. Nearshore areas adjacent to the mainland exhibit markedly lower wave energy potential. Autumn and winter seasons witness significantly larger wave heights in the central and northern areas of the South China Sea than spring and summer. The Gated Recurrent Unit （GRU） model outperforms Long Short-Term Memory （LSTM） and Multilayer Perceptron （MLP） architectures in prediction accuracy. Minimal prediction errors were achieved with a single hidden layer combined with Adam or RMSProp optimization algorithms. This research provides novel perspectives and practical references for wave energy prediction methodologies.

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