Separation method of wind-wave and swell based on the multilayer perceptron
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摘要: 风涌浪分离是研究风浪、涌浪各自特性的基础，但受限于海浪谱数据的匮乏，基于海浪谱的风涌浪分离方法难以普及应用，有效的解决办法是采用波浪观测中容易获取的基本波要素进行风涌浪分离。现有方法无法利用基本波要素全面计算出风浪、涌浪的比例及其特征参数，为此本文将机器学习引入到风涌浪分离中，以多层感知器模型为基础，提出了一种利用基本波要素、风要素准确计算出风涌浪参数的方法。该方法需要每个测站提供至少466笔、建议766笔及以上的实测波浪数据作为训练样本，适用于台湾海峡3个测站，在计算精度上显著优于基于海浪频谱的传统风涌浪分离方法，可为本海域缺乏海浪谱的测站提供替代性的风涌浪计算方案，有助于扩大实测风涌浪资料的来源，进而加强风涌浪分布特性以及预警预报研究。Abstract: Separation of wind-wave and swell is the basis for studying the respective characteristics of wind-wave and swell. However, due to the lack of wave spectrum data, it is difficult to popularize and apply separation methods based on wave spectrums. An effective solution is to use wave observations that are easy to obtain, namely basic wave elements to separate wind-wave and swell. Existing methods cannot use basic wave elements to comprehensively calculate the proportions and characteristic parameters of wind-wave and swell. For this reason, this paper introduces machine learning into the separation of wind-wave and swell. Based on the multi-layer perceptron model, a method using wave elements and wind elements to accurately estimate wind-wave and swell parameters is proposed. This method requires each station to provide at least 466 training samples of wave data and 766 or more training samples are recommended. The method is suitable for 3 stations in the Taiwan Strait with its accuracy significantly better than traditional methods based on wave spectrums. The proposed method can provide alternative calculation schemes of wind-wave and swell for stations lacking wave spectrums in this sea area. It helps expand the source of measured data of wind-wave and swell, therefore strengthening the research on the characteristics and early warning and forecasting of wind-wave and swell.
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Key words:
- separation of wind-wave and swell /
- Taiwan Strait /
- machine learning /
- swell /
- wind-wave
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图 1 波浪站位置（a）和二维谱法的可视化展示（b）
Fig. 1 Location of the buoy （a） and visualization of 2D spectrum method （b）
图 2 风浪有效波高计算模型的结构示意图
Fig. 2 Structure diagram of calculation model of the significant height of wind-wave
图 4 各多层感知器模型的平均相对误差（MRE）随模型结构的变化
子图a、b、c、d分别对应于模型一、二、三、四
Fig. 4 Variation of the mean relative error (MRE) of each multilayer perceptron model with the model structure
Subgraphs a, b, c and d correspond to model 1, 2, 3 and 4 respectively
图 5 多层感知器模型在不同测站的误差指标
Fig. 5 Error indices of multilayer perceptron models in different stations
图 6 风浪平均周期预报值与实测值对比
Fig. 6 Contrast of predicted mean wind-wave period with measured data
图 7 各多层感知器模型的平均绝对误差（a）和平均相对误差（b）随训练样本量的变化
Fig. 7 Variation of mean absolute error (a) and mean relative error (b) of multilayer perceptron models with training set size
图 8 各多层感知器模型的平均绝对误差随测试样本量的变化
Fig. 8 Variation of mean absolute error of multilayer perceptron models with test set size
图 9 台风期间多层感知器法风涌浪参数计算值与实测值对比
Fig. 9 Comparision of measured wave data with wind-wave and swell computation values using multilayer perceptron method during typhoon passage
图 10 不同方法计算出的波参数与实测数据对比
Fig. 10 Comparison of the wave parameters calculated by different methods with measured data
表 1 测站数据信息表
Tab. 1 Statistics of station data information
站名 数据时间范围 方向谱数据量 风、浪要素数据量 重叠时段数据总量 水深/m M1 2016年7月1日至9月30日 2 030 2 208 2 030 58 M2 2016年7月1日至9月15日 1 185 1 826 1 185 25 M3 2016年7月1日至9月27日 2 130 2 130 2 130 27 
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表 2 多层感知器模型的输入输出设置
Tab. 2 Input and output settings of multilayer perceptron model
模型命名 输入因子 输出因子 模型1 风速，风向，混合浪波高，混合浪波向 风浪有效波高 模型2 风速，风向，混合浪波高，混合浪波向 涌浪有效波高 模型3 风速，风向，混合浪周期，混合浪波向 风浪平均周期 模型4 风速，风向，混合浪周期，混合浪波向 涌浪平均周期
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表 3 四变量输出模型与单变量输出模型的平均相对误差（MRE）对比
Tab. 3 Comparision of mean relative error (MRE) between 4 output model and 1 output model
预报模式 风浪有效波高 涌浪有效波高 风浪平均周期 涌浪平均周期 四变量预报 12.7% 6.6% 13.1% 6.6% 单变量预报 10.1% 4.4% 10.3% 4.9%
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表 4 不同风涌浪分离方法的误差指标
Tab. 4 Error indices of different separation methods of wind-wave and swell
目标变量 风涌浪分离方法 相关系数 平均绝对误差 平均相对误差 风浪有效波高 MLP法 0.97 0.08 m 10.1% PM法 0.98 0.28 m 50.9% WH法 0.83 0.28 m 20.3% 改进的WH法 0.86 0.54 m 56.7% JP法 0.77 0.30 m 50.2% 林伊楠等[11]的方法 0.96 0.34 m 45.6% 涌浪有效波高 MLP法 0.99 0.05 m 4.4% PM法 0.60 0.42 m 38.9% WH法 0.92 0.20 m 17.9% 改进的WH法 0.76 0.63 m 60.6% JP法 0.55 0.24 m 18.5% 林伊楠等[11]的方法 0.83 0.43 m 41.7% 风浪平均周期 MLP法 0.82 0.47 s 10.3% PM法 0.57 0.85 s 17.0% WH法 0.18 1.20 s 25.9% 改进的WH法 0.25 1.00 s 26.1% JP法 0.45 2.96 s 67.3% 林伊楠等[11]的方法 0.37 0.83 s 18.9% 涌浪平均周期 MLP法 0.92 0.35 s 4.9% PM法 0.43 2.28 s 34.6% WH法 0.80 0.77 s 11.8% 改进的WH法 0.73 3.37 s 52.5% JP法 0.37 1.53 s 22.4% 林伊楠等[11]的方法 0.28 2.52 s 40.4%
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