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 MConvLSTM-Attention method is trained and tested on regional ionospheric maps (RIMs) for three years (2015 to 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 regional ionospheric maps (RIMs). In addition, the MConvLSTM-Attention prediction model shows good generalization performance and relatively good stability and high precision during both geomagnetic quiet and storm time.