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A wavelet - Particle swarm optimization - Extreme learning machine hybrid modeling for significant wave height prediction

机译：小波粒子群优化 - 高层波高预测的极限学习机混合模型

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摘要

Predictions of Significant wave height (Hs) of oceans is highly required in advance for coastal and ocean engineering applications. Therefore, this study aims to precisely predict the ocean wave height via developing a novel hybrid algorithm. Wavelet, Particle Swarm Optimization (PSO), and Extreme Learning Machine (ELM) methods were used and integrated to design the wavelet-PSO-ELM (WPSO-ELM) model for estimating the wave height belongs to coastal and deep-sea stations. A comparative analysis among the ELM, Kernel ELM (KELM), and PSO-ELM models were performed with and without wavelet integration. In addition, wave height prediction time leads up to 72 h were assessed. The meteorological data, including wave height for one year, have been utilized and evaluated to design and validate the proposed model; the data obtained from buoys situated off the southeast coast of the US. The results demonstrated that the WPSO-ELM outperforms other models to predict the wave height in both hourly and daily lead times; in addition, the wavelet increased the accuracy of the prediction models, with the goal that coefficient of determination (R-2), willmott's index of agreement (d), root mean square error (RMSE), and mean absolute error (MAE) were obtained for the lead time 12 h equivalent 0.794, 0.784, 0.374 m, and 0.297 m, respectively for the WPSO-ELM, and 0.643, 0.736, 0.495 m and 0.363 m, respectively for the PSO-ELM. Comparing the obtained results revealed the better performance of the WPSO-ELM model in predicting wave height for coastal and deep-sea regions up to 36 h' lead times.
机译：对沿海和海洋工程应用的高度要求海洋的显着波浪高度（HS）的预测。因此，本研究旨在通过开发新的混合算法精确地预测海浪高度。使用小波，粒子群优化（PSO）和极端学习机（ELM）方法，并集成设计小波-PSO-ELM（WPSO-ELM）模型，用于估计波浪高度属于沿海和深海站。 ELM，核心ELM（KELM）和PSO-ELM模型的比较分析是在没有小波积分的情况下进行的。另外，评估波高预测时间最高可达72小时。已经利用和评估了一年内的气象数据，包括波高，为设计和验证所提出的模型;从浮标上获得的数据位于美国东南海岸。结果表明，WPSO-ELM优于其他模型，以预测每小时和每日交货时间的波高;此外，小波提高了预测模型的准确性，目标是确定系数（R-2），WillMott的协议索引（d），根均方误差（Rmse）和平均误差（MAE）是对于WPSO-ELM，分别为WPSO-ELM的铅时间12h等效0.794,0.794,0.374m和0.297μm，分别为PSO-ELM。比较所获得的结果揭示了WPSO-ELM模型在预测沿海和深海地区的波浪高达36小时的潮流时代的更好性能。

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  • 来源
    《Ocean Engineering》 |2020年第1期|107777.1-107777.14|共14页
  • 作者

    Kaloop Mosbeh R.; Kumar Deepak; Zarzoura Fawzi; Roy Bishwajit; Hu Jong Wan;

  • 作者单位

    Incheon Natl Univ Dept Civil & Environm Engn Incheon South Korea|Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon South Korea|Mansoura Univ Publ Works & Civil Engn Dept Mansoura Egypt;

    Natl Inst Technol Patna Dept Civil Engn Patna Bihar India;

    Mansoura Univ Publ Works & Civil Engn Dept Mansoura Egypt;

    Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India;

    Incheon Natl Univ Dept Civil & Environm Engn Incheon South Korea|Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon South Korea;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wave height; Prediction; Wavelet; Particle swarm optimization; Extreme learning machine;

    机译：波高;预测;小波;粒子群优化;极端学习机;

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