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Optimizing machine learning for space weather forecasting and event classification using modified metaheuristics

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Abstract

Space weather profoundly impacts Earth and its surrounding space environment, necessitating improved prediction to safeguard critical infrastructure such as communication and satellites. Solar flares can disrupt communications and pose radiation risks to airline passengers. While traditional methods offer rough estimates of solar activity trends, the potential of artificial intelligence in this domain warrants exploration. This study addresses this research gap by evaluating the performance of recurrent neural networks (RNNs) for sunspot forecasting and assessing the suitability of extreme gradient boosting (XGBoost) for solar event classification. Two publicly available datasets serve as the foundation for this research. To enhance algorithm performance through optimal hyperparameter selection, metaheuristic optimizers are employed. A unique contribution is the introduction of a modified particle swarm optimization algorithm, specifically tailored to the study’s needs. Two experiments were conducted: In the first, RNNs predicted sunspot occurrence up to three steps ahead. The best-performing model, optimized using the introduced modified metaheuristic, achieved an impressive \(R^2\) value of 0.840448, surpassing competing algorithms. In the second experiment, XGBoost models assessed solar flare severity, with the top model again optimized by the modified metaheuristic, achieving an accuracy of 0.981565. This novel approach highlights the potential for enhancing solar activity forecasting techniques and offers valuable insights into feature impacts on model decisions, thereby advancing our understanding of space weather.

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Data availability

The datasets analyzed during the current study are available in the following URLs https://www.kaggle.com/datasets/robervalt/sunspots and https://zenodo.org/record/4603412.

Notes

  1. https://www.kaggle.com/datasets/robervalt/sunspots.

  2. https://zenodo.org/record/4603412.

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Funding

This research was supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, under Grant No. III-44006. This research was supported by the Science Fund of the Republic of Serbia, Grant No. 7373, Characterizing crises-caused air pollution alternations using an artificial intelligence-based framework - crAIRsis.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, Belgrade, 11000, Serbia

    Luka Jovanovic, Nebojsa Bacanin, Miodrag Zivkovic & Marko Sarac

  2. Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11010, Serbia

    Vladimir Simic

  3. College of Engineering, Department of Industrial Engineering and Management, Yuan Ze University, Yuandong Rd., Zhongli Dist., Taoyuan City, 320315, Taiwan

    Vladimir Simic

  4. Modern College of Business and Science, 3 Bawshar St., 133, Muscat, Oman

    Joseph Mani

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  1. Luka Jovanovic
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Contributions

NB and LJ—experiments and writing original draft; VS—review and editing; JM—validation; MZ—visualization and writing original draft; MS—data acquisition and preparation.

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Correspondence to Nebojsa Bacanin.

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Jovanovic, L., Bacanin, N., Simic, V. et al. Optimizing machine learning for space weather forecasting and event classification using modified metaheuristics. Soft Comput 28, 6383–6402 (2024). https://doi.org/10.1007/s00500-023-09496-9

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