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Rainfall prediction model using soft computing technique

Wong, K.W., Wong, P.M., Gedeon, T. and Fung, C.C. (2003) Rainfall prediction model using soft computing technique. Soft Computing, 7 (6). pp. 434-438.

Link to Published Version: http://dx.doi.org/10.1007/s00500-002-0232-4
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Abstract

Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.

Publication Type: Journal Article
Murdoch Affiliation: School of Information Technology
Publisher: Springer Berlin
Copyright: © Springer-Verlag 2003
URI: http://researchrepository.murdoch.edu.au/id/eprint/927
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