TEC Variation Prediction through AI and Space Weather Analysis

Global Navigation Satellite Systems (GNSS) require very precise Total Electron Content (TEC) variation prediction to be operating good, especially in low-latitude regions where ionospheric disturbances are more efficient. Space weather events like solar activity, geomagnetic storms, and ionospheric irregularities have powerful effect on TEC. This research proposes unique method that is AI-driven also data analysis and machine learning models are being used in various fields to predict thermal emission coefficients (TEC) variations. For improving the TEC accuracy, this approach includes data from various sources such as solar indices, geomagnetic activity, and ionospheric characteristics. Hybridmodels and various machine learning techniques are used to represent the non-linear correlations between ionospheric dynamics and space weather phenomena. The challenges that current systems face are low generalization across different geographic locations, uneven data quality, and limited prediction accuracy.