A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling

Amal H. Alharbi¹, Marwa M. Eid^2,*, Nima Khodadadi³, Ebrahim A. Mattar⁴, Sayed Elkenawy^5,6
1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt
3 Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
4 College of Engineering, University of Bahrain, Sakhir, Bahrain
5 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, Isa Town, Bahrain
6 Jadara Research Center, Jadara University, Irbid, Jordan
* Corresponding Author: Marwa M. Eid. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.080044

Received 02 February 2026; Accepted 15 April 2026; Published online 18 May 2026

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Abstract

Seismic time series forecasting remains challenging due to the nonlinearity, non-stationarity, and noise of earthquake data, and because deep learning models are sensitive to preprocessing and hyperparameter settings. Although recent studies have improved neural architectures and optimization techniques, preprocessing is often treated as a fixed or manually designed stage, with limited integration into model optimization. To address this, this paper proposes an integrated, data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction, specifically forecasting earthquake magnitude from seismic catalog time-series data, with experiments conducted on Canadian seismic records. The method uses a Large Language Model to guide data preparation and feature engineering, rather than fully automate them, and applies deep learning-based forecasting with the N-HITS architecture, optimized via metaheuristic-assisted feature selection and hyperparameter tuning. The Football Optimization Algorithm (FbOA), employed as a metaheuristic optimization strategy in this study, is evaluated and compared with several well-known optimizers under identical conditions. The results show significant performance gains, with FbOA achieving superior accuracy, robustness, and convergence compared to baseline and competing methods. Notably, error metrics are reduced (MSE 3.10×10−7, RMSE 5.57×10−3), with high performance indicators (r=0.982, R2=0.979, NSE =0.981, WI =0.985). These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains.