Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks

In this paper, we applied a recurrent neural network to predict a wave height and a peak wave period for next 24 hours from only those last 24 hours. We adopted LSTM as the network structure and used statistic gradient decent method and adaptive moment estimation method as the learning methods. It w...

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Main Authors: Osawa, K., Yamaguchi, H., Umair, M., Hashmani, M.A., Horio, K.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29863 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097531635&doi=10.1109%2fICCI51257.2020.9247805&partnerID=40&md5=9d21601ccbc698e2e67315629ee058d3
http://eprints.utp.edu.my/29863/
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spelling utp-eprints.298632022-03-25T03:04:45Z Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks Osawa, K. Yamaguchi, H. Umair, M. Hashmani, M.A. Horio, K. In this paper, we applied a recurrent neural network to predict a wave height and a peak wave period for next 24 hours from only those last 24 hours. We adopted LSTM as the network structure and used statistic gradient decent method and adaptive moment estimation method as the learning methods. It was difficult to estimate short-time fluctuations because only the wave height and period data were used as inputs, but it was shown that the wave height and peak wave period within the next 2 hours can be predicted with an accuracy within 20 percent in error. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097531635&doi=10.1109%2fICCI51257.2020.9247805&partnerID=40&md5=9d21601ccbc698e2e67315629ee058d3 Osawa, K. and Yamaguchi, H. and Umair, M. and Hashmani, M.A. and Horio, K. (2020) Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks. In: UNSPECIFIED. http://eprints.utp.edu.my/29863/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In this paper, we applied a recurrent neural network to predict a wave height and a peak wave period for next 24 hours from only those last 24 hours. We adopted LSTM as the network structure and used statistic gradient decent method and adaptive moment estimation method as the learning methods. It was difficult to estimate short-time fluctuations because only the wave height and period data were used as inputs, but it was shown that the wave height and peak wave period within the next 2 hours can be predicted with an accuracy within 20 percent in error. © 2020 IEEE.
format Conference or Workshop Item
author Osawa, K.
Yamaguchi, H.
Umair, M.
Hashmani, M.A.
Horio, K.
spellingShingle Osawa, K.
Yamaguchi, H.
Umair, M.
Hashmani, M.A.
Horio, K.
Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
author_sort Osawa, K.
title Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
title_short Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
title_full Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
title_fullStr Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
title_full_unstemmed Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks
title_sort wave height and peak wave period prediction using recurrent neural networks
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097531635&doi=10.1109%2fICCI51257.2020.9247805&partnerID=40&md5=9d21601ccbc698e2e67315629ee058d3
http://eprints.utp.edu.my/29863/
_version_ 1741197311200985088
score 11.62408

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