Total Electron Content Forecasting in Low Latitude Regions of India: Machine and Deep Learning Synergy

Our goal is to determine the parameters that affect the total electron content in the ionosphere (TEC) by comparing data with numerous models. Free charged particles are present in the plasma of ionised gas that makes up the terrestrial ionosphere. It is created when solar radiation ionises. IRI is present in the Earth's atmosphere and is a component of gaseous elements. The magnetosphere's dense ions and charged particles have an effect on the speed of radio-frequency signals. Therefore, one of the most significant causes of inaccuracy in GNSS (Global Navigation Satellite System) positioning and navigation services is magnetospheric delay. Furthermore, the ionosphere's quantitative influence clarifies the total electron content (TEC), which is the total number of electrons gathered per square metre during the journey from a spacecraft to a GNSS receiver. We are attempting to determine the relative performance of various machine learning techniques, including Gradient Boosting Model, LSTM, and Linear Regression, on the TEC prediction problem. The experimental investigation demonstrates that the gradient boosting regressor produced the minimum loss followed by a legitimate coefficient of determination when comparing all models.