Different types of PCA-NN model for TEC with space weather parameters as predictors: advantages and disadvantages of different NN algorithms

A PCA-NN model for the total electron content (TEC) for the midlatitudinal region (Iberian Peninsula) presented here uses the principal component analysis (PCA) to decompose TEC variations into different modes and to reconstruct/forecast amplitudes of these modes using neural networks (NN) with different sets of space weather parameters as predictors.

Feedforward, convolutional and recurrent NN algorithms are tested with different sets of predictors. The performance of the models is tested on 3.5 years of observational data obtained at the declined phase of the 24^th solar cycle, which allows us to estimate the models’ performance in relation to the solar activity level. The advantages and disadvantages of different NN algorithms are discussed.