Designing empirical fourier decomposition reinforced with multiscale increment entropy and deep learning to forecast dry bulb air temperature
Article
Article Title | Designing empirical fourier decomposition reinforced with multiscale increment entropy and deep learning to forecast dry bulb air temperature |
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ERA Journal ID | 214071 |
Article Category | Article |
Authors | Diykh, Mohammed, Ali, Mumtaz, Labban, Abdulhaleem H., Prasad, Ramendra, Jamei, Mehdi, Abdulla, Shahab and Farooque, Aitazaz Ahsan |
Journal Title | Results in Engineering |
Journal Citation | 26 |
Article Number | 104597 |
Number of Pages | 20 |
Year | 2025 |
Publisher | Elsevier BV |
Place of Publication | Netherlands |
ISSN | 2590-1230 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.rineng.2025.104597 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2590123025006759 |
Abstract | Accurate prediction of dry bulb air temperature (DBTair) is significant to determine the state of humid air and supporting experts in the environmental sector. Traditional machine learning based approaches struggle to deliver accurate predictions when temperature is suddenly fluctuated during extreme weather conditions. This paper aims to design an intelligent model namely MEFD-MSIE-FCNN to forecast DBTair which integrates multivariate empirical Fourier decomposition (MEFD), multiscale increment entropy (MSIE), and FCSM model that integrates a fully connected neural network FCNN with long short-term memory (LSTM) to forecast DBTair. The multivariant time series of each predictor variable is passed through the MEFD to extract mutual features across multivariant time series and deliver multivariable-aligned modes. Then, the MSIE is extracted to form a feature final matrix to represent mutual information from multivariant time series. Finally, the features set is sent to the FCSM to forecast multistep ahead DBTair using goodness-of-fit statistical metrics for two regions in Saudi Arabia. The proposed model showed highest accuracy for Jazan station (RMSE=2.120, MAE=2.912, RSE=0.123, ECC=0.971, WIA=0.981, CC=0.982), and Jeddah station (RMSE=2.131, MAE=2.921, RSE=0.113, ECC=0.969, WIA=0.979, CC=0.980). A comprehensive comparison is made against state-of-the art benchmarking models, concluding that there is a noticeable improvement in model's performance in terms of AME, ECC, CC, WIA, RMSE and correlation coefficient. The proposed FCSM can be helpful for many applications such as improving weather prediction, preventing climate risks, energy consumption, water resources management and agricultural industry. Additionally, the proposed model can support decision makers and industries in the environmental sector to make informed decisions to mitigate the effects of climate change. |
Keywords | Air temperature; Dry bulb; Deep learning; Forecasting; Deep learning; MEFD; MSIE |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Byline Affiliations | University of Thi-Qar, Iraq |
Al-Ayen University, Iraq | |
University of Prince Edward Island, Canada | |
UniSQ College | |
King Abdulaziz University, Saudi Arabia | |
University of Fiji, Fiji |
https://research.usq.edu.au/item/zwzvq/designing-empirical-fourier-decomposition-reinforced-with-multiscale-increment-entropy-and-deep-learning-to-forecast-dry-bulb-air-temperature
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