Solar Flare Prediction Using Hybrid CNN-Model

This paper introduces a hybrid convolutional neural network (CNN) approach to solve the issues given by unpredictable solar flares in space weather forecasting. The methodology, which uses the Solar Dynamics Observatory (SDO) benchmark dataset, combines the robust feature extraction capabilities of VGGNet-16 with the computational efficiency of LeNet. The SDO dataset’s input data is carefully preprocessed and shrunk to fit the input size of VGGNet-16, which processes the data to extract complex feature maps capturing minor solar activity patterns. These maps are then transformed to 32x32 to meet the input requirements of LeNet, enabling for accurate classification of solar flares into their appropriate classifications. The experimental results show that the hybrid CNN system outperforms standard approaches in terms of prediction accuracy. The combination of VGGNet-16 feature extraction with LeNet classification improves accuracy while also ensuring computational economy, which is critical for real-time applications. This study represents a big step forward in space weather forecasting, demonstrating the connection between deep learning and heliophysics. The suggested model advances our understanding of solar phenomena by exposing complex patterns underlying solar flares and bridging the gap between observational data and computer models. The discovery holds potential for real-time space weather monitoring, which is critical for satellite operations and terrestrial communication systems. The methodology’s efficacy highlights its potential for enhancing astrophysical data processing and space weather forecasting skills, emphasizing the synergy between the technological advancement and scientific research in the field of astrophysics.