Improving Dry-Bulb Air Temperature Prediction Using a Hybrid Model Integrating Genetic Algorithms with a Fourier–Bessel Series Expansion-Based LSTM Model

Hussein Alabdally, Mumtaz Ali (), Mohammad Diykh, Ravinesh C. Deo, Anwar Ali Aldhafeeri (), Shahab Abdulla and Aitazaz Ahsan Farooque
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Hussein Alabdally: UniSQ College, University of Southern Queensland, Springfield, QLD 4350, Australia
Mumtaz Ali: UniSQ College, University of Southern Queensland, Springfield, QLD 4350, Australia
Mohammad Diykh: UniSQ College, University of Southern Queensland, Springfield, QLD 4350, Australia
Ravinesh C. Deo: Artificial Intelligence Applications Laboratory, School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
Anwar Ali Aldhafeeri: Department of Mathematics and Statistics, Faculty of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
Shahab Abdulla: UniSQ College, University of Southern Queensland, Springfield, QLD 4350, Australia
Aitazaz Ahsan Farooque: Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

Forecasting, 2025, vol. 7, issue 3, 1-25

Abstract: The dry-bulb temperature is a critical parameter in weather forecasting, agriculture, energy management, and climate research. This work proposes a new hybrid prediction model (FBSE-GA-LSTM) that integrates the Fourier–Bessel series expansion (FBSE), genetic algorithm (GA), and long short-term memory (LSTM) networks together to predict the dry-bulb air temperature. The hybrid model FBSE-GA-LSTM utilises the FBSE to decompose time series data of interest into an attempt to remove the noise level for capturing the dominant predictive patterns. Then, the FBSE is embedded into the GA method for the best feature selection and dimension reduction. To predict the dry-bulb temperature, a new model (FBSE-GA-LSTM) was used by hybridising a proposed model FBSE-GA with the LSTM model on the time series dataset of two different regions in Saudi Arabia. For comparison, the FBSE and GA models were hybridised with a bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU) models to obtain the hybrid FBSE-GA-BiLSTM, FBSE-GA-GRU, and FBSE-GA-BiGRU models along with their standalone versions. In addition, benchmark models, including the climatic average and persistence approaches, were employed to demonstrate that the proposed model outperforms simple baseline predictors. The experimental results indicated that the proposed hybrid FBSE-GA-LSTM model achieved improved prediction performance compared with the contrastive models for the Jazan region, with a mean absolute error (MAE) of 1.458 °C, a correlation coefficient (R) of 0.954, and a root mean squared error (RMSE) of 1.780 °C, and for the Jeddah region, with an MAE of 1.459 °C, an R of 0.952, and an RMSE of 1.782 °C, between the predicted and observed values of dry-bulb air temperature.

Keywords: genetic algorithm; Fourier Bessel series expansion; dry bulb temperature; long short-term memory (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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