A hybrid fusion of the recurrent neural network and bidirectional long short-term memory for wind speed prediction in the South China Sea

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% compared with BiLSTM. For MAPE, the improvements were 84.3% over ARIMA, 38.8% over RNN, 40.3% over LSTM, and 32.1% over BiLSTM. To the best of our knowledge, this is the first study to integrate RNN and BiLSTM for multi-scale wind speed prediction in the South China Sea, demonstrating improved predictive accuracy over both deep learning and statistical baselines. These findings highlight the model’s operational potential for energy planning, navigation safety, and weather risk management.