Ionospheric Total Electron Content (TEC) forecasting using deep learning approach

Zaidi, Zufifuzaimie Azreen (2022) Ionospheric Total Electron Content (TEC) forecasting using deep learning approach. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Total Electron Content (TEC) is one of the physical quantities that can be derived from global positioning system (GPS) data and provides an indication of ionospheric variability. TEC variations have significant effects on radio communications, applications involving navigational systems, GPS surveying and space weather. In order to understand these effects, there is a need to develop forecasting techniques. Therefore, this study aims to determine the Total Electron content (TEC) in the Ionosphere at a particular time. It also aims to develop a deep learning model utilizing MATLAB for forecasting the Ionospheric Total Electron Content (TEC) and investigate the relationship between Ionospheric Total Electron Content (TEC), solar activity, and geomagnetic field. In conclusion, a model capable of reliably predicting the Total Electron Contain (TEC) using a deep learning algorithm has been developed. Radio operators can use the predicted value to determine the ionospheric condition in advance, especially during ionospheric disturbances.

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