Spread-F prediction model for the equatorial Chumphon station, Thailand

Abstract

This work proposes a range spread-F (RSF) prediction model using the neural network (NN) over the equatorial Chumphon (CPN) region in Thailand. The RSF model is constructed by using five input spaces including the diurnal variations, seasonal variations, geographic latitude, solar flux index (F10.7), and magnetic index (Ap). The RSF NN model is trained with three years of RSF data during 2013 to 2015 from Chumphon (CPN) station (Latitude = 10.7°N, Longitude = 99.4°E) and the performance of the proposed RSF NN model is validated using the dataset of 2016. As a result, the RSF NN model achieves 98.3% accuracy of all correct predictions even with the limited available data. The results show that the proposed NN model yields a lower RSF probability than the actual observation by about 7.3%, but the overestimation of the proposed NN model is 2.5% in both the equinoxes and solstices. In addition, we discover that the IRI-2016 model mostly overestimates the RSF probability when compared with the actual observation for all seasons in 2016, particularly, in equinoctial months over Chumphon station.