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Rainfall prediction model using soft computing technique
Journal article   Peer reviewed

Rainfall prediction model using soft computing technique

K.W. Wong, P.M. Wong, T. Gedeon and C.C. Fung
Soft Computing, Vol.7(6), pp.434-438
2003
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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.

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UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#6 Clean Water and Sanitation
#13 Climate Action
#14 Life Below Water

Source: InCites

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Collaboration types
Domestic collaboration
Citation topics
8 Earth Sciences
8.19 Oceanography, Meteorology & Atmospheric Sciences
8.19.7 Hydroclimatic Modeling
Web Of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
ESI research areas
Computer Science
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