Neural Networks forecasting architectures for rainfall in the rain-fed Sectors in Sudan

Feed forward Multilayer Perceptron (MLP) Neural Networks are universal approximators. Weight adjustment of the connectionist model is crucial to architectures that model systems behavior. This paper developed a neural network for hydrological purposes. Two architectures were developed, investigated, and tested for forecasting rainfall in the rain-fed Sectors in Sudan. A monthly architecture and a decade architecture are developed with backpropagation feedforward neural network. The two architectures are found to be efficient for forecasting rainfall in these sectors.