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A modular spatial interpolation technique for monthly rainfall prediction in the Northeast region of Thailand

Kajornrit, J., Wong, K.W. and Fung, C.C. (2014) A modular spatial interpolation technique for monthly rainfall prediction in the Northeast region of Thailand. In: Boonkrong, S., Unger, H. and Meesad, P., (eds.) Recent Advances in Information and Communication Technology. Springer International Publishing, Switzerland, pp. 53-62.

Link to Published Version: http://dx.doi.org/10.1007/978-3-319-06538-0_6
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Abstract

Monthly rainfall spatial interpolation is an important task in hydrological study to comprehensively observe the spatial distribution of the monthly rainfall variable in the study area. A number of spatial interpolation methods have been successfully applied to perform this task. However, those methods mainly aim at achieving satisfactory interpolation accuracy and they disregard the interpolation interpretability. Without interpretability, human analysts will not be able to gain insight of the model of the spatial data. This paper proposes an alternative approach to achieve both accuracy and interpretability of the monthly rainfall spatial interpolation solution. A combination of fuzzy clustering, fuzzy inference system, genetic algorithm and modular technique has been used. The accuracy of the proposed method has been compared to the most commonly-used methods in geographic information systems as well as previously proposed method. The experimental results showed that the proposed model provided satisfactory interpolation accuracy in comparison with other methods. Besides, the interpretability of the proposed model could be achieved in both global and local perspectives. Human analysts may therefore understand the model from the derived model’s parameters and fuzzy rules.

Publication Type: Book Chapter
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer International Publishing
Copyright: 2014 Springer International Publishing Switzerland
Notes: Series Title: Advances in Intelligent Systems and Computing; Vol. 265
URI: http://researchrepository.murdoch.edu.au/id/eprint/23730
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