Seasonal rainfall forecast using a Neo-Fuzzy Neuron Model

Knowledge about the seasonal rainfall for some regions of Brazil is essential, due to the dependency of agriculture and for a correct management of water resources. For this, linear and nonlinear models are commonly used for seasonal rainfall prediction, some of them are based on Artificial Neural Networks, which have proved to have a great potential for this purpose. Following this idea, this work presents a seasonal rainfall forecast model based on a neuro-fuzzy technique, called Neo-Fuzzy Neuron Model, that showed a better performance, in terms of root mean square error and correlation coefficient between predicted and real output, when compared with a dynamic downscaling model using the Regional Spectral Model. The experimental results show the effectiveness of the proposed method in predicting the first four trimesters from 2002 up to the current year.