Artificial Neural Networks are becoming important tools in a wide variety of meteorological applications. However, the estimation of rainfall has continued to be a very difficult and complex problem to solve. A single multi-layered back propagation neural network used on complex problems involving different sub-tasks will often show strong inter sub-task interference effects that lead to slow learning and poor generalisation. The modular neural network approach is to decompose the classification task into simpler sub-tasks, each one being handled by a separate module. The modules are then combined to produce an overall solution demonstrating a very natural way to solve a complex problem. One modular neural network strategy is an ensemble of neural networks where each network is a whole problem classifier. This paper investigates the development of an ensemble of neural networks for the rainfall estimation problem. The results demonstrate that an ensemble of neural networks has the potential to achieve an improvement in performance for rainfall estimations.
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