An artificial neural network model in predicting VTEC over central Anatolia in Turkey

Abstract

This research investigates the capability of artificial neural networks to predict vertical total electron content (VTEC) over central Anatolia in Turkey. The VTEC dataset was derived from the 19 permanent Global Positioning System (GPS) stations belonging to the Turkish National Permanent GPS Network-Active (TUSAGA-Aktif) and International Global Navigation Satellite System Service (IGS) networks. The study area is located at 32.6°E-37.5°E and 36.0°N-42.0°N. Considering the factors inducing VTEC variations in the ionosphere, an artificial neural network (NN) with seven input neurons in a multi-layer perceptron model is proposed. The KURU and ANMU GPS stations from the TUSAGA-Aktif network are selected to implement the proposed neural network model. Based on the root mean square error (RMSE) results from 50 simulation tests, the hidden layer in the NN model is designed with 41 neurons since the lowest RMSE is achieved in this attempt. According to the correlation coefficients, absolute and relative errors, the NN VTEC provides better predictions for hourly and quarterly GPS VTEC. In addition, this paper demonstrates that the NN VTEC model shows better performance than the global IRI2016 model. Regarding the spatial contribution of the GPS network to TEC prediction, the KURU station performs better than ANMU station in fitting with the proposed NN model in the station-based comparison.