Metaheuristic Optimized Extreme Gradient Boosting for Solar Flare Prediction

Intense electromagnetic activity on the Sun’s surface can lead to events known as solar flares that often lead to mass ejections and other solar events. Sufficiently powerful solar events can make it to the Earth and interfere with telecommunication systems. Notably energetic events can even affect satellite and ground communication infrastructure potentially causing massive damage. Extensive monitoring systems in combination with robust forecasting techniques can be used as an early warning system to minimize potential damage. This work proposes a method based on extreme gradient boosting (XGBoost) applied to solar flare classification. As the performance of XGBoost heavily relies on adequate hyperparameter selection, a modified metaheuristic algorithm is also introduced to tune network hyperparameters. The introduced approach has been evaluated on a real-world dataset and a comparative analysis has been carried out against several contemporary algorithms tackling the same task under identical conditions. The introduced approach has attained promising outcomes.