Integration of Fuzzy C-Means and Artificial Neural Network for Short-Term Localized Rainfall Forecast in Tropical Climate

  • Noor Zuraidin Mohd-SafarEmail author
  • David Ndzi
  • David Sanders
  • Hassanuddin Mohamed Noor
  • Latifah Munirah Kamarudin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


This paper evaluates the performance of a rainfall forecasting model. In this paper Artificial Neural Network (ANN) and Fuzzy C-Means (FCM) clustering algorithm are combined and used to forecast short-term localized rainfall in tropical climate. State forecast (raining or not raining) and value forecast (rain intensity) are tested using a number of trained networks. Different types of ANN structured were trained with a combination of multilayer perceptron with back propagation network. Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient training algorithm are used in the network training. Each neurons uses linear, logistic sigmoid and hyperbolic tangent sigmoid as transfer function. Input parameter preliminary analysis, data cleaning and FCM clustering were used to prepare input data for the ANN forecast model. Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed have been used as input parameters. The predicted rainfall forecast for 1 to 6 h ahead are compared and analyzed. 1 h ahead for state and value forecast yield high accuracy. Result shows that, the combined of FCM-ANN forecast model produces better accuracy compared to a basic ANN forecast model.


Artificial neural network Fuzzy c-means ANN FCM Rainfall forecast Rainfall prediction Tropics Neural network Soft computing Meteorology Tropical climate Soft clustering 


  1. 1.
    Pradhan, B.: Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J. Spat. Hydrol. 9, 1–18 (2009)Google Scholar
  2. 2.
    Shahi, A.: An effective fuzzy C-mean and type-2 fuzzy. J. Theor. Appl. Inf. Technol. 5, 556–567 (2009)Google Scholar
  3. 3.
    Maqsood, I., Khan, M., Abraham, A.: An ensemble of neural networks for weather forecasting. Neural Comput. Appl. 13, 112–122 (2004)CrossRefGoogle Scholar
  4. 4.
    Hu, M.J.C., Root, H.E.: An adaptive data processing system for weather forecasting. J. Appl. Meteorol. 3(5), 513–523 (1964)CrossRefGoogle Scholar
  5. 5.
    Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13(8), 1413–1425 (2008)CrossRefGoogle Scholar
  6. 6.
    Klent Gomez Abistado, C.N.A., Maravillas, E.A.: Weather forecasting using artificial neural network and bayesian network. J. Adv. Computat. Intell. Intell. Inf. 18(5), 812–817 (2014)CrossRefGoogle Scholar
  7. 7.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Bezdek, J.C.: FCM: the fuzzy C-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)CrossRefGoogle Scholar
  9. 9.
    Lu, Y., Ma, T., Yin, C., Xie, X., Tian, W., Zhong, S.: Implementation of the fuzzy C-means clustering algorithm in meteorological data. Int. J. Database Theory Appl. 6(6), 1–18 (2013)CrossRefGoogle Scholar
  10. 10.
    Vega-corona, A.: ANN and Fuzzy c-means applied to environmental pollution prediction, pp. 1–6 (2012)Google Scholar
  11. 11.
    Howard Demuth, M.B., Hagan, M.: Neural network toolbox user’s guide (2012)Google Scholar
  12. 12.
    Neural Networks Training Based on Differential Evolution Algorithm Compared with Other Architectures for Weather Forecasting34. J. Comput. Sci. Netw. Secur. 9(3), 92–99 (2009)Google Scholar
  13. 13.
    Cheng, B., Titterington, D.M.: Neural Networks: A Review from a Statistical Perspective. Stat. Sci. 9(1), 2–30 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Goss, D.F.E.: Forecasting with neural networks: an application using bankruptcy data. Inf. Manage. 24(3), 159–167 (1993)CrossRefGoogle Scholar
  15. 15.
    Abraham, A., Philip, N.S., Joseph, K.B.: Will we have a wet summer ? In: Soft Computing Models for Long-term Rainfall Forecasting (1992)Google Scholar
  16. 16.
    Charalambous, C.: Conjugate gradient algorithm for efficient training of artificial neural networks. IEE Proc. G Circuits, Devices Syst. 139(3), 301 (1992)CrossRefGoogle Scholar
  17. 17.
    Foresee, F.D., Hagan, M.T.: Gauss-Newton approximation to Bayesian learning. In: Proceedings of International Conference on Neural Networks (ICNN 1997) (1997)Google Scholar
  18. 18.
    Pellakuri, V., Rajeswara Rao, D., Lakshmi Prasanna, P., Santhi, M.V.B.T.: A conceptual framework for approaching predictive modeling using multivariate regression analysis vs artificial neural network. J. Theor. Appl. Inf. Technol. 77(2), 287–290 (2015)Google Scholar
  19. 19.
    Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Lecture Notes in Mathematics, pp. 105–116. Springer, Berlin (1978)Google Scholar
  20. 20.
    MacKay, D.J.C.: A practical bayesian framework for back propagation networks. Neural Comput. 4(3), 448–472 (1992)CrossRefGoogle Scholar
  21. 21.
    MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)CrossRefzbMATHGoogle Scholar
  22. 22.
    Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. Science 49(CS-94–125), 64 (1994)Google Scholar
  23. 23.
    Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)CrossRefGoogle Scholar
  24. 24.
    Hauke, J., Kossowski, T.: Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011)CrossRefGoogle Scholar
  25. 25.
    Yuan, H.L., Gao, X.G., Mullen, S.L., Sorooshian, S., Du, J., Juang, H.M.H.: Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network. Weather Forecast. 22(6), 1287–1303 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Noor Zuraidin Mohd-Safar
    • 1
    Email author
  • David Ndzi
    • 1
  • David Sanders
    • 1
  • Hassanuddin Mohamed Noor
    • 1
  • Latifah Munirah Kamarudin
    • 2
  1. 1.School of EngineeringUniversity of PortsmouthPortsmouthUK
  2. 2.School of Computer and Communication EngineeringUniversiti Malaysia PerlisArauMalaysia

Personalised recommendations