Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class Prediction

TL;DR

This study compares machine learning algorithms like Random Forest, KNN, and XGBoost for classifying solar flares into four categories, using dimensionality reduction and cross-validation to improve space weather prediction accuracy.

Contribution

It introduces a novel combination of binary and multiclass classification with different dimensionality reduction levels for solar flare prediction.

Findings

Random Forest and XGBoost outperform KNN in classification accuracy.

Increased dimensionality improves model performance.

The methodology enhances future space weather forecasting accuracy.

Abstract

Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun's atmosphere. These energetic bursts of electromagnetic radiation can release up to 10^32 erg of energy, impacting space weather and posing risks to technological infrastructure and therefore require accurate forecasting of solar flare occurrences and intensities. This study evaluates the predictive performance of three machine learning algorithms: Random Forest, k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost) for classifying solar flares into 4 categories (B, C, M, X). Using the dataset of 13 SHARP parameters, the effectiveness of the models was evaluated in binary and multiclass classification tasks. The analysis utilized 8 principal components (PC), capturing 95% of data variance, and 100 PCs, capturing 97.5% of variance. Our approach uniquely combines binary and multiclass classification with different levels of dimensionality reduction, an innovative methodology not previously explored in the context of solar flare prediction. Employing a 10-fold stratified cross-validation and grid search for hyperparameter tuning ensured robust model evaluation. Our findings indicate that Random Forest and XGBoost consistently demonstrate strong performance across all metrics, benefiting significantly from increased dimensionality. The insights of this study enhance future research by optimizing dimensionality reduction techniques and informing model selection for astrophysical tasks. By integrating this newly acquired knowledge into future research, more accurate space weather forecasting systems can be developed, along with a deeper understanding of solar physics.