A Short-Term Forecasting Model of Ionospheric hmF2 Based on Wavelet Transform and a Neural Network in China

The peak height of the ionospheric F2 layer (hmF2) is a critical parameter in ionospheric physics and high-frequency radio wave propagation research. This study presents a backpropagation neural network (BPNN) enhanced by wavelet transform (WT) decomposition for one-hour-ahead hmF2 forecasting. The WT method decomposes and reconstructs the hmF2 time series, preserving its primary structural characteristics. Subsequently, the BPNN provides high-accuracy predictions. The model is trained and evaluated using 2014 hmF2 measurements from four observation stations in China. Utilizing only hmF2 data, the model produces accurate one-hour-ahead forecasts. The predicted values closely align with observed diurnal variations and exhibit lower fluctuations than those of the IRI and standalone BPNN models. On the test set, the proposed model achieves an average RMSE of 17.16 km, which is 10.10 km and 8.39 km lower than the IRI and BPNN models, respectively. The average RRMSE is 5.72%, representing reductions of 2.88% and 2.64% compared to the IRI and BPNN models, respectively. These findings indicate that the hybrid model is well-suited for the Chinese region and substantially enhances short-term hmF2 forecast accuracy.