Rainfall rate prediction based on artificial neural networks for rain fade mitigation over earth-satellite link

In this paper, we present a model for rainfall rate prediction 30 seconds ahead of time using an artificial neural network. The resultant predicted rainfall rate can then be used in determining an appropriate fade counter-measure, for instance, digital modulation scheme ahead of time, to keep the bit error rate (BER) on the link within acceptable levels to allow constant flow of data on the link during a rain event. The approach used in this paper is pattern recognition technique that considers historical rainfall rate patterns over Durban (29.8587°S, 31.0218°E). The resultant prediction model is found to predict an immediate future rain rate when given three adjacent historical rain rates. For our model validation, error analysis via root mean square (RMSE) technique on our prediction model results show that resultant errors lie within acceptable values at different rain events within different rainfall regimes.