A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

Abstract

The rapid evolutions in artificial intelligence and the machine learning era have significantly improved accuracy for ionospheric space weather forecasting models. The ionospheric total electron content (TEC) forecasting is necessary to alert global navigation satellite system (GNSS) users about ionospheric space weather influences on satellite-receiver radio communications. Precise modeling and forecasting of the ionospheric TEC are critical for reliable and accurate GNSS applications. In this article, a deep-learning-based bidirectional long short-term memory (bi-LSTM) algorithm is implemented for 26 global positioning system stations TEC data over the Indian region. The bi-LSTM ionospheric TEC forecasting maps are generated and compared with ANN and LSTM models during both geomagnetic quiet and disturbed periods. The potential of bi-LSTM networks in time-sequence processing is improved by having forward and backward connections. The bi-LSTM results are demonstrated with and without solar and geomagnetic indices as input to the network. This work's outcome would help to develop an ionospheric weather alert system for GNSS users.

Keywords

• Adjusted spherical harmonics function (ASHF)
• artificial intelligence (AI)
• bidirectional long short-term memory (bi-LSTM)
• deep learning (DL)
• forecast
• global positioning system (GPS)