Machine learning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations.

Global Navigation satellite systems (GNSS) are predominantly affected by Ionospheric space weather. The GNSS signal delays due to ionospheric Total Electron Content (TEC) can be forecasted using advances in the emerging mathematics tools and algorithms. Machine learning algorithms such as Gaussian Process Regression (GPR) is considered in the present paper to implement in the forecasting of low-latitude ionospheric conditions. The GPS receiver data is obtained for 8 years (2009–2016) during 24th solar cycle from International GNSS Services (IGS) station located in Bengaluru (Geographical latitude: 12.97° N, Geographical longitude: 77.59° E), India. The performance of GPR model is validated using statistical parameters such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and correlation coefficient. The results of the proposed GPR model were compared with the Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model during solar maximum period and descending phase of 24th solar cycle. The experiment results are evident that GPR model is significantly providing the promising results in forecasting the ionospheric time delays for GNSS signals. The outcome of the work can be useful to develop web based Ionospheric TEC forecasting system to alert the GNSS users. • The GPS-TEC variability is investigated during solar maximum period and descending phase of 24th solar cycle. • The performance of ARMA, ANN and GPR models are evaluated in forecasting the ionospheric delays for GPS signals. • GPR model has better predicted TEC than the existing ARMA and ANN models during geomagnetic quiet and storm periods. • Kernel-based approach of GPR model is well suitable to forecast the ionospheric TEC variations over low latitudes. [ABSTRACT FROM AUTHOR]