Abstract:

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 (NNs) 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 two neural networks architectures in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Comparisons are also made between those neural networks architectures at different training and testing scenarios. Simulation results for each scenario is demonstrate the effectives of both neural network architecture.