AI-Driven TEC Prediction Using Spatial Weather Insights: A Web-Based Solution
Ananya Srivastava, Aryan Kaushik, Manas Rai, Harsh Khatter · 2025
Total Electron Content (TEC) is a vital parameter for the analysis of the ionosphere, which has major implications for satellite communications, positioning, and space weather. This research paper describes an AI-based predictive model that uses spatial weather information for the reliable prediction of TEC changes. This paper details a novel system, which combines state-of-the-art machine learning approaches with a web-based interface for users to visualize, analyze, and interact with instantaneous TEC predictions. Providing a dynamic and scalable solution, this study aims to overcome the limitations of traditional Indian methodologies, hence increasing the accuracy and accessibility of prediction. Above all, this study has transformative potential in connecting theoretical innovations with practical applications, establishing a precedent in ionospheric research while also inspiring innovations in vital domains such as the aerospace sector, telecommunications, and disaster management.