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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, J., Heap, A.D.: A review of spatial interpolation methods for environmental scientists. Geoscience Australia, record 2008/23 (2008)
Chang, T.: Introduction to geographic information systems, 3rd edn. McGraw-Hill, Singapore (2006)
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)
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)
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)
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)
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)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(23), 16–20 (1984)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)
Holland, J.H.: Adaptation in natural and artificial system. University of Michigan Press, Ann Arbor (1975)
ChrisTseng, H., Almogahed, B.: Modular neural networks with applications to pattern profiling problems. Neurocomputing 72, 2093–2100 (2009)
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)
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)
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)
Tutmez, B., Tercan, A.E., Kaymak, U.: Fuzzy modeling for reserve estimation based on spatial variability. Mathematical Geology 39(1) (2007)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kajornrit, J., Wong, K.W., Fung, C.C. (2014). A Modular Spatial Interpolation Technique for Monthly Rainfall Prediction in the Northeast Region of Thailand. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-06538-0_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06537-3
Online ISBN: 978-3-319-06538-0
eBook Packages: EngineeringEngineering (R0)