Abstract
Spatial interpolation is a method to create spatial continuous surface from observed data points. Spatial interpolation is important to water management and planning because it could provide estimation of rainfall at unobserved area. This paper proposes a methodology to analyze and establish an integrated intelligent spatial interpolation model for monthly rainfall data. The proposed methodology starts with determining the optimal number of sub-regions by means of standard deviation analysis and artificial neural networks. Once the optimal number of sub-regions is determined, a Mamdani fuzzy inference system is generated by fuzzy c-means and then optimized by genetic algorithm. Four case studies were used to evaluate the accuracy of the established models and compared with trend surface analysis and artificial neural networks. The experimental results demonstrated that the proposed methodology provided reasonable interpolation accuracy and the methodology gave human understandable fuzzy rules to human analysts.
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