Evolving expert neural networks for meteorological rainfall estimations

Various techniques for estimating meteorological parameters have been developed over the past few years that involve artificial neural networks. However, the estimation of rainfall has continued to be a very difficult and complex problem to solve. Data mining techniques are needed to extract the important information from the vast amount of meteorological data available. A single multi-layer backpropagation 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. Dividing the system up into several different "expert networks" each specialising in a different sub-task can reduce this interference at the cost of having to combine the outputs from each of the experts. This paper investigates the technique of dividing the rainfall estimation problem into a number of such experts each specialising in a particular rainfall band (i.e. low, medium or high rain). Results demonstrate that expert networks can be successfully developed which result in both improved individual classifications and improved overall classification accuracy.