eprintid: 14963
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/49/63
datestamp: 2023-11-10 03:29:33
lastmod: 2023-11-10 03:29:33
status_changed: 2023-11-10 01:58:16
type: article
metadata_visibility: show
creators_name: Alqushaibi, A.
creators_name: Abdulkadir, S.J.
creators_name: Rais, H.M.
creators_name: Al-Tashi, Q.
creators_name: Ragab, M.G.
creators_name: Alhussian, H.
title: Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction
ispublished: pub
note: cited By 21
abstract: 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). © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2021
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106559174&doi=10.3390%2fjmse9050524&partnerID=40&md5=9eae8e4b25e4dd4adc2a4c741358d521
id_number: 10.3390/jmse9050524
full_text_status: none
publication: Journal of Marine Science and Engineering
volume: 9
number: 5
refereed: TRUE
issn: 20771312
citation:   Alqushaibi, A. and Abdulkadir, S.J. and Rais, H.M. and Al-Tashi, Q. and Ragab, M.G. and Alhussian, H.  (2021) Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction.  Journal of Marine Science and Engineering, 9 (5).   ISSN 20771312