MATLAB GUI for Forecasting the Ionospheric F2 Layer’s Critical Frequency

This paper examines the prediction of the ionospheric F2 layer’s critical frequencies (foF2) using a backpropagation neural network model in conjunction with particle swarm optimization (BPNN–PSO) for various solar and geomagnetic activities. The critical frequency data were taken from an ionosonde located at Universiti Tun Hussein Onn Malaysia (UTHM) in Johor (1.86° N, 103.80° E). The models’ predictive ability for foF2 was investigated under various solar activity and geomagnetic storm circumstances. The developed graphical user interface (GUI) was used to forecast the critical frequency of the ionospheric F2 layer. The forecasted data was then assessed using mean absolute percentage error (MAPE) and root-mean-square error (RMSE). The model performs best during high solar activity, with an RMSE of 0.2003 MHz and a MAPE of 4.2263%. Meanwhile, the model also performs best in moderate storms, with an RMSE of 0.3255 MHz and a MAPE of 7.5888%.