文章基本信息

标题：Enhanced Weight-Optimized Recurrent Neural Networks Based on Sine Cosine Algorithm for Wave Height Prediction
本地全文：下载
作者：Alawi Alqushaibi ; Said Jadid Abdulkadir ; Helmi Md Rais 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2021
卷号：9
期号：5
页码：524
DOI：10.3390/jmse9050524
语种：English
出版社：MDPI AG
摘要：Constructing offshore and coastal structures with the highest level of stability and lowest cost, as well as the prevention of faulty risk, is the desired plan that stakeholders seek to obtain. The successful construction plans of such projects mostly rely on well-analyzed and modeled metocean data that yield high prediction accuracy for the ocean environmental conditions including waves and wind. Over the past decades, planning and designing coastal projects have been accomplished by traditional static analytic, which requires tremendous efforts and high-cost resources to validate the data and determine the transformation of metocean data conditions. Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. Three neural network models named: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (VRNN), and Gated Recurrent Network (GRU) are enhanced, instead of random weight initialization, SCA generates weight values that are adaptable to the nature of the data and model structure. Besides, a Grid Search (GS) is utilized to automatically find the best models’ configurations. To validate the performance of the proposed models, metocean datasets have been used. The original LSTM, VRNN, and GRU are implemented and used as benchmarking models. The results show that the optimized models outperform the original three benchmarking models in terms of mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE).