基于多周期趋势分解和两级融合策略的浪高预测方法

Wave Height Prediction Method Based on Multi-Period Trend Decomposition and Two-Level Fusion Strategy

不同海区的近岸海浪浪高具有明显差异性。针对当前大部分时间序列预测模型缺乏对不同地区(多源)浪高预测的适应性难题,提出了一种基于局部加权回归的多周期趋势分解(Seasonal and Trend decomposition using Loess,STL)和两级融合策略的浪高预测模型,简称为MSTL-WH(Multiple STL-Wave Height)。结合多源近岸浪高时间序列的多周期性、非线性和非平稳性的特点,首先利用周期图法提取多源近岸浪高数据集中的4个主要周期,并基于主要周期进行多次STL分解,将复杂的原始浪高序列分解为周期项、趋势项和余项;然后利用长短期记忆网络(Long Short Term Memory,LSTM)并结合两级融合策略,搭建近岸浪高预测网络;最后使用自注意力机制重新调整权重并输出未来12 h的浪高值。通过与当前主流时间序列预测方法对比,验证了所提方法在多源近岸浪高序列预测中具有较好的实用性和更低的预测误差。

Nearshore wave height exhibits significant difference in different sea areas,and most of the time-series prediction models lack the adaptability to wave height prediction in different regions(i.e.,multi-source wave height prediction).To solve this problem,we propose a wave height prediction model based on multi-period trend decomposition(STL)with locally weighted regression and two-level fusion strategy(MSTL-WH).Combining the multi-periodicity,nonlinearity and non-stationarity of nearshore wave height time series,periodogram method is used to extract four major periods of the wave height,and multiple STL decomposition was performed based on the major periods,decomposing the original wave height series into seasonal,trend and residual parts.Then,we used the Long Short-Term Memory Network(LSTM)combined with two-level fusion strategy build a nearshore wave height prediction network.Finally,self-attention mechanism was used to readjust the weights and the wave height was calculated for the next 12 hours.Compared with the existing time-series forecasting methods,the method we proposed has better practicability and smaller errors in nearshore wave height forecasting.