Monthly median fof2 modelling cost 251 area by neural networks

Abstract

The use of a neural network to model the monthly median ionospheric foF2 frequencies has been tested in order to establish a new long-term prediction procedure to support ionospheric radiowave propagation at frequencies above 2 MHz. The neural networks (NN) have been trained with the foF2 measured data from the European ionospheric stations in three separate cases: (i) a single station model at Poitiers (46°0 N, 00°0 E) build with the classical multi-layer perceptron (MLP) with 3 inputs: hour, month and solar activity index; (ii) a modular neural network with the same inputs; (iii) a 2D model build over Europe with additional inputs: geographical latitude and longitude. In this last case, the problem of the spatial interpolations between ionospheric stations is also studied. The results are compared with those of the classical PRIME and ITU-R models.