An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting

Abstract

The ionosphere is vital for satellite navigation and radio communication, but observational limitations necessitate ionospheric forecasting. The least squares collocation (LSC) method is commonly used for global navigation satellite system (GNSS)-based global ionospheric forecasting, though its accuracy and stability need improvement. This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance. Using seven years of historical data, the model provides stable forecasts for the following year. Tests from 2015 to 2020 show that optimization reduces root mean square error (RMSE) by 10.159%–16.363% compared to the unoptimized method. The encoder-decoder ConvLSTM-B model achieves the best performance, lowering RMSE by 2.031%–8.547% compared to the ConvLSTM-A model. These results highlight the effectiveness of the proposed approach in improving ionospheric forecast accuracy.