Prediction of GNSS-Based Regional Ionospheric TEC Using a Multichannel ConvLSTM With Attention Mechanism

Monitoring and predicting ionospheric space weather is important for global navigation satellite system (GNSS) navigation, positioning, and communication. Ionospheric total electron content (TEC) is a vital indicator to measure ionospheric space weather. This study utilizes a multichannel convolutional long short-term memory (ConvLSTM) with attention mechanism to predict ionospheric TEC maps considering the relevance of physical observations for TEC variations. The multichannel ConvLSTM with attention mechanism (MConvLSTM-Attention) method is trained and tested on regional ionospheric maps (RIMs) for three years (2015-2017) by using GNSS observations from the Crustal Movement Observation Network of China (CMONOC). The experimental results show that the root-mean-square error (RMSE) values of MConvLSTM-Attention model during quiet, moderate, and geomagnetic storm periods are 2.58, 2.60, and 3.21 TECU, respectively. Also, the MConvLSTM-Attention model performs better than MConvLSTM, ConvLSTM, and international reference ionosphere (IRI) 2016 models in predicting RIMs. In addition, the MConvLSTM-Attention prediction model shows good generalization performance, relatively good stability, and high precision during both geomagnetic quiet and storm time.