Open Access
ENHANCED IDENTIFICATION OF EQUATORIAL PLASMA BUBBLES IN AIRGLOW IMAGERY VIA 2D PRINCIPAL COMPONENT ANALYSIS AND INTERPRETABLE AI
4
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, India
Abstract
Equatorial Plasma Bubbles (EPBs) are ionospheric irregularities that disrupt communication and navigation systems, especially in low-latitude regions. This study presents an enhanced framework for identifying EPBs in airglow imagery by integrating two-dimensional Principal Component Analysis (2D-PCA) with interpretable artificial intelligence (AI) techniques. The proposed approach efficiently extracts spatial features from airglow images and leverages interpretable AI models to improve classification accuracy while maintaining transparency in decision-making. Experimental validation using real-world airglow datasets demonstrates the superiority of the 2D-PCA and interpretable AI combination over traditional detection methods in terms of accuracy, robustness, and explainability. The findings pave the way for more reliable space weather monitoring and early warning systems.
Keywords
Equatorial Plasma Bubbles,
airglow imagery,
2D Principal Component Analysis,
interpretable AI
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