primary topic: A hybrid fusion of the recurrent neural network and bidirectional long short-term memory for wind speed prediction in the South China Sea
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HTML Summary of #20677 A hybrid fusion of the recurrent neural network and bidirectional long short-term memory for wind speed prediction in the South China Sea
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primary topic: A hybrid fusion of the recurrent neural network and bidirectional long short-term memory for wind speed prediction in the South China Sea

A hybrid fusion of the recurrent neural network and bidirectional long short-term memory for wind speed prediction in the South China Sea
abstract: Wind speed prediction in the South China Sea is crucial for enhancing maritime safety, supporting operational planning, and optimizing economic activities in sectors such as offshore energy, shipping, and disaster preparedness. In recent years, the statistical auto-regressive integrated moving average (ARIMA) model and advanced deep learning models such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, and Bidirectional LSTM (BiLSTM) have shown strong potential for time series forecasting due to their capacity to model temporal dependencies. However, these models often face limitations in simultaneously capturing rapid short-term fluctuations and long-term temporal patterns in meteorological data. To address this challenge, we propose a novel hybrid architecture, h-RNN-BiLSTM, which integrates the short-term dynamic modeling capability of RNN with the long-range bidirectional dependency modeling of BiLSTM. This fusion enables multi-scale temporal pattern learning, thereby improving forecasting accuracy. The model is evaluated using two widely recognized spatiotemporal datasets: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Forecast System (GFS). Data preprocessing, including missing value imputation and standardization, was applied to ensure data consistency and improve model convergence. Experiments were conducted in two settings: (i) short-term datasets from GFS and ECMWF, and (ii) long-term ECMWF datasets. The performance of h-RNN-BiLSTM was compared against baseline RNN, LSTM, BiLSTM, and the ARIMA model using root mean square error (RMSE) and mean absolute percentage error (MAPE) as evaluation metrics. Results demonstrate that the proposed model consistently outperforms the deep learning baselines and ARIMA, with the most significant gains observed for the long-term ECMWF dataset. Specifically, the model reduced error by 99.7 compared with ARIMA, 70.3 compared with RNN, 30.7 compared with LSTM, and 37.6 …
author list: authors
status: peerReviewed
status: published
volume: 11
date: 2025
is part of: repository
is part of: PeerJ Computer Science
type: AcademicArticle
type: Article
type: ArticleEPrint
type: EPrint

PeerJ Inc.
type: Organization

Farahida Hanim Mausor
family name: Mausor
given name: Farahida Hanim
type: Person

Abdul Kadir Jumaat
family name: Jumaat
given name: Abdul Kadir
type: Person

Jasni Mohamad Zain
family name: Mohamad Zain
given name: Jasni
type: Person

Murizah Kassim
family name: Kassim
given name: Murizah
type: Person

Siti Sara Ibrahim
family name: Ibrahim
given name: Siti Sara
type: Person

Nazleeni Samiha Haron
family name: Haron
given name: Nazleeni Samiha
type: Person

Abdul Basit
family name: Basit
given name: Abdul
type: Person

Nor Farisha Muhamad Krishnan
family name: Muhamad Krishnan
given name: Nor Farisha
type: Person

Mohd Azdi Maasar
family name: Maasar
given name: Mohd Azdi
type: Person

Jafreezal Jaafar
family name: Jaafar
given name: Jafreezal
type: Person

Marina Yusoff
family name: Yusoff
given name: Marina
type: Person

Hafiza Zoya Mojahid
family name: Mojahid
given name: Hafiza Zoya
type: Person

Nooritawati Md Tahir
family name: Md Tahir
given name: Nooritawati
type: Person

PeerJ Computer Science
issn: 23765992
type: Collection