An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia
An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia
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DOI：
10.3390/rs13081456
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发表时间：
2021-04-01
期刊：
影响因子：
5
通讯作者：
Brown, Jason
中科院分区：
文献类型：
--
作者：
Raj, Nawin;Brown, Jason
使用先进的深度学习（DL）算法在相对较短的时间内预测沿海海浪的有效波高，可以生成有关其影响和行为的重要信息。这对于诸如搜索和救援以及沿着海岸环境的波浪涌等事件的事先规划和决策至关重要。短期24小时预报可以为相关团体提供足够的时间采取预防措施。本研究利用海浪的零上越波周期（Tz）、峰值能量波周期（Tp）、海表温度（SST）和显著滞后等特征进行有效波高（Hs）预报。该数据集于2014年至2019年在澳大利亚昆士兰州主要城市的沿海地区以沿着30分钟的间隔收集。本研究的新奇在于开发和应用了一种高精度的混合Boruta随机森林（BRF）-集成经验模式分解（EEMD）-双向长短期记忆（BiLSTM）算法来预测有效波高（Hs）。EEMD-BiLSTM模型优于所有其他模型，具有更高的Pearson相关性（R）值0.9961（BiLSTM-0.991，EEMD-支持向量回归（SVR）-0.9852，SVR-0.9801）和相对较低的相对均方误差（RMSE）0.0214（BiLSTM-0.0248，EEMD-SVR-0.043，SVR-0.0507），同样，Pearson相关性（R）值较高，为0.9965（BiLSTM-0.9903，EEMD-SVR-0.9953，SVR-0.9935）和相对较低的RMSE 0.0413（BiLSTM-0.075，EEMD-SVR-0.0481，SVR-0.057）对于黄金海岸。
Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)-ensemble empirical mode decomposition (EEMD)-bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD-BiLSTM model outperforms all other models with a higher Pearson's correlation (R) value of 0.9961 (BiLSTM-0.991, EEMD-support vector regression (SVR)-0.9852, SVR-0.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM-0.0248, EEMD-SVR-0.043, SVR-0.0507) for Cairns and similarly a higher Pearson's correlation (R) value of 0.9965 (BiLSTM-0.9903, EEMD-SVR-0.9953, SVR-0.9935) and comparatively lower RMSE of 0.0413 (BiLSTM-0.075, EEMD-SVR-0.0481, SVR-0.057) for Gold Coast.