Accurate weather predictions are important for planning our day-to-day activities. In recent years, a large literature has evolved on the use of artificial neural networks (ANNs) in many forecasting applications. Neural networks are particularly appealing because of their ability to model an unspecified non-linear relationship between weather variables. This paper evaluates three neural networks architectures with different training techniques, in this context: the popular multilayer perceptron (MLP), the radial basis function network (RBF) and feed forward neural networks which were trained by differential evolution algorithm. Different testing and training scenarios are presented. Those scenarios are designed to obtain the most suitable one for weather predication at different neural network architectures. Simulation results for each scenario demonstrate the effectives of both neural network architectures and its associated training algorithm.

Weather Forecasting, Neural Networks, Radial basis functions, Differential Evolution