Forecasting of ionospheric vertical TEC 1-h ahead using a genetic algorithm and neural network

A hybrid back propagation artificial neural network (ANN) with a genetic algorithm (GA) is built to predict 1-h ahead vertical total electron content (TEC) of single station in China. An ionospheric TEC forecast model was developed based on TEC data from four dual-frequency GPS stations of BJFS (39.61°N, 115.89°E), XIAN (34.18°N, 108.99°E), WUHN (30.53°N, 114.36°E), and KUNM (25.03°N, 102.80°E) in both 2007 and 2011. The results show the mean relative errors of the model are smaller than 10% and the root mean square (RMS) error is less than 1 TECU at mid-high-latitudes during the period of low solar activity in 2007. Some large RMS errors with more than 5 TECU can be observed at low latitude during the medium solar activity period of 2011. Compared to the traditional BP algorithm, the new algorithm is a promising and reliable alternative forecast technology for ionospheric TEC.