A Modular Spatial Interpolation Technique for Monthly Rainfall Prediction in the Northeast Region of Thailand

  • Jesada Kajornrit
  • Kok Wai Wong
  • Chun Che Fung
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)


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.


Monthly Rainfall Spatial Interpolation Modular Technique Fuzzy Clustering Fuzzy Inference System Genetic Algorithm Interpretable Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Li, J., Heap, A.D.: A review of spatial interpolation methods for environmental scientists. Geoscience Australia, record 2008/23 (2008)Google Scholar
  2. 2.
    Chang, T.: Introduction to geographic information systems, 3rd edn. McGraw-Hill, Singapore (2006)Google Scholar
  3. 3.
    Li, J., Heap, A.D., Potter, A., Daniell, J.J.: Application of machine learning methods to spatial interpolation of environmental variables. Environ Modell. Softw. 26, 1647–1659 (2011)CrossRefGoogle Scholar
  4. 4.
    Kajornrit, J., Wong, K.W., Fung, C.C.: An integrated intelligent technique for monthly rainfall spatial interpolation in the northeast region of Thailand. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 384–391. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Di Piazza, A., Lo Conti, F., Noto, L.V., Viola, F., La Loggia, G.: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. Int. J. Appl. Earth OBS 13, 396–408 (2011)CrossRefGoogle Scholar
  6. 6.
    Zhou, S., Gan, J.Q.: Low level interpretability and high level interpretability: a unifed view of data-driven interpretable fuzzy system modelling. Fuzzy Set Syst. 159, 3091–3131 (2008)CrossRefGoogle Scholar
  7. 7.
    Cordon, O.: A history review of evolutionary learning methods for Mamdani-type fuzzy rule-based system: Designing interpretable genetic fuzzy system. Int. J. Approx. Reason 52, 894–913 (2011)CrossRefGoogle Scholar
  8. 8.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(23), 16–20 (1984)Google Scholar
  9. 9.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)CrossRefGoogle Scholar
  10. 10.
    Holland, J.H.: Adaptation in natural and artificial system. University of Michigan Press, Ann Arbor (1975)Google Scholar
  11. 11.
    ChrisTseng, H., Almogahed, B.: Modular neural networks with applications to pattern profiling problems. Neurocomputing 72, 2093–2100 (2009)CrossRefGoogle Scholar
  12. 12.
    Kajornrit, J., Wong, K.W.: Cluster validation methods for localization of spatial rainfall data in the northeast region of Thailand. In: Proc. 12th International Conference on Machine Learning and Cybernetics (2013)Google Scholar
  13. 13.
    Hoppner, F., Klawonn, F.: Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression. In: Proc. 4th International Conference on Knowledge-based Intelligent Engineering Systems and Allied Tech (KES), pp. 162–165 (2000)Google Scholar
  14. 14.
    Wang, X.Z., Yeung, D.S., Tsang, E.C.C.: A comparative study on heuristic algorithms for generating fuzzy decision trees. IEEE Trans. Systems Man Cybernet, Part B 31(2), 215–226 (2001)CrossRefGoogle Scholar
  15. 15.
    Tutmez, B., Tercan, A.E., Kaymak, U.: Fuzzy modeling for reserve estimation based on spatial variability. Mathematical Geology 39(1) (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zimmerman, D., Pavlik, C., Ruggles, A., Armstrong, M.P.: An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology 31, 375–390 (1999)CrossRefGoogle Scholar
  17. 17.
    Luo, W., Taylor, M.C., Parkerl, S.R.: A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int. J. Climatol. 28, 947–959 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jesada Kajornrit
    • 1
  • Kok Wai Wong
    • 1
  • Chun Che Fung
    • 1
  1. 1.School of Engineering and Information TechnologyMurdoch UniversityMurdochAustralia

Personalised recommendations