Earth Planets Space, Vol. 64 (No. 6), pp. 473-483, 2012
Kornyanat Watthanasangmechai1, Pornchai Supnithi1, Somkiat Lerkvaranyu1, Takuya Tsugawa2, Tsutomu Nagatsuma2, and Takashi Maruyama2
1Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang,
Bangkok 10520, Thailand
2National Institute of Information and Communications Technology, Nukui-kita, 4-2-1, Koganei, Tokyo 183-8795, Japan
(Received June 3, 2010; Revised March 2, 2011; Accepted May 9, 2011; Online published July 27, 2012)
This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station.
Key words: Neural network, total electron content, low solar activity period, IRI-2007 model, GPS.