**3. Conclusion**

A regional VTEC model over Nigeria was developed using the method of computer neural networks and GPS-VTEC data from 14 stations spanning the period from years 2011 to 2016. A total of seven input layer neurons (namely, Year, Day of Year, Hour of Day, Geomagnetic Longitude, Geomagnetic Latitude, SSN, and DST indices) were used to learn the studied output (GPS-TEC). By simulating 20 different networks that differed in their number of hidden layer neurons, the network with 6 hidden layer neuron was determined to be the best in terms of minimizing the prediction errors (using the RMSE as criterion for measuring the prediction error).

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The neural network model was demonstrated to be proficient in predicting the VTEC variation patterns in terms of diurnal variations, seasonal variations, long-term solar cycle variations, and spatial variations across Nigeria.

When compared with two popular global ionospheric models (the NeQuick and the IRI-Plas), predictions from the neural network model was observed to be more accurate in terms of closeness to the GPS-VTEC values. Typical RMSEs for the neural network model predictions were between 1.3 and 10.8 TECU, the mean RMSE was 5.6 TECU. For the IRI-Plas model, the RMSEs were between 8.5 and 12.4 TECU, and the mean was 11.2 TECU. For the NeQuick, the RMSEs were between 2.8 and 11.0 TECU, and the mean was 6.7 TECU. The work done in this chapter further validates neural networks as excellent candidates for modeling of ionospheric parameters.
