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

  • Noor Zuraidin Mohd-Safar
  • David Ndzi
  • David Sanders
  • Hassanuddin Mohamed Noor
  • Latifah Munirah Kamarudin
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)

Abstract

This paper proposes and analyses the applicability of integrating Fuzzy C-Means (FCM) and artificial neural network (ANN) in rainfall forecasting. The algorithm of ANN and FCM clustering are integrated and applied to forecast short-term localized rainfall in tropical weather. Rainfall forecasting in this paper is divided into state forecast (raining or not raining) and rainfall rate forecast. Various type of back propagation extended network with hidden layers of ANN structured were trained. Training algorithm of Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used and trained. Transfer function in each neuron uses linear, logistic sigmoid and hyperbolic tangent sigmoid. Initial statistical analysis of weather parameter, data pre-processing approach and FCM clustering method were used to organize input data for the ANN forecast model. Input parameters such as atmospheric pressure, temperature, dew point, humidity and wind speed have been used. One to six hour predicted rainfall forecast are compared and analyzed. The result indicates that the integrated of FCM-ANN forecast model yield 80% for 1 h forecast.

Keywords

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

References

  1. Abraham, A., Philip, N.S., Joseph, K.B. In: Will We Have a Wet Summer ? Soft Computing Models for Long-term Rainfall Forecasting (1992)Google Scholar
  2. Bezdek, J.C.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)MathSciNetCrossRefGoogle Scholar
  3. Bora, D.J.: A comparative study between fuzzy clustering algorithm and hard clustering algorithm 10(2), 108–113 (2014)Google Scholar
  4. Chau, K.W., Wu, C.L.: A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J. Hydroinformatics 12(4), 458–473 (2010)CrossRefGoogle Scholar
  5. Department of Irrigation and Drainage (DID) Manual (Volume 1—Flood Management). Department of Irrigation and Drainage, Malaysia, 1 (2009)Google Scholar
  6. Deser, C., Phillips, A., Bourdette, V.: Uncertainty in climate change projections: the role of internal variability 527–546 (2012)Google Scholar
  7. Dunn, J.C.: A fuzzy relative of the process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)MathSciNetCrossRefMATHGoogle Scholar
  8. Foresee, F.D., Hagan, M.T.: Guass-Newton approximation to bayesian learning. In: Proceedings of the International Conference on Neural Networks, Houston, Texas, pp. 1930–1935. IEEE (1997)Google Scholar
  9. Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences. Atmos. Environ. 32(14), 2627–2636 (1998)CrossRefGoogle Scholar
  10. Goss, D.F.E.: Forecasting with neural networks: an application using bankruptcy data. Inf. Manage. 24(3), 159–167 (1993)CrossRefGoogle Scholar
  11. Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O.: Neural Network Design, vol. 20. PWS Publishing Company, Boston (1996)Google Scholar
  12. Hamidi, Z.S., Shariff, N.N.M., Monstein, C.: Understanding climate changes in Malaysia through space weather study. Int. Lett. Nat. Sci. 13, 9–16 (2014)CrossRefGoogle Scholar
  13. 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
  14. 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
  15. Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for rainfall forecasting in Bangkok. Thai. Hydrol. Earth Syst. Sci. 13(8), 1413–1425 (2008)CrossRefGoogle Scholar
  16. Irwin, S., Srivastav, R., Slobodan, P.S.: Instructions for Operating the Proposed Regionalization Tool Cluster-FCM Using Fuzzy C-Means Clustering and L-Moment Statistics (2011)Google Scholar
  17. Karlik, B.: The positive effects of fuzzy c-means clustering on supervised learning classifiers. Int. J. Artif. Intell. Expert Syst. (IJAE) 7, 1–8 (2016)Google Scholar
  18. Klent Gomez Abistado, C.N.A., Maravillas, E.A.: Weather forecasting using artificial neural network and Bayesian network. J. Adv. Comput. Intell. Intell. Inf. 18(5), 812–817 (2014)CrossRefGoogle Scholar
  19. Krzhizhanovskaya, V.V., Shirshov, G.S., Melnikova, N.B., Belleman, R.G., Rusadi, F.I., Broekhuijsen, B.J., Meijer, R.J.: Flood early warning system: design, implementation and computational modules. Procedia Comput. Sci. 4, 106–115 (2011)Google Scholar
  20. Lim, J.T., Samah, A.A.: Weather and Climate of Malaysia. University of Malaya Press (2004)Google Scholar
  21. Lohani, A.K., Goel, N.K., Bhatia, K.K.S.: Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall-runoff modelling under different input domains. Hydrol. Process. 25(2), 175–193 (2011)CrossRefGoogle Scholar
  22. 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
  23. MacKay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448–472 (1992a)CrossRefGoogle Scholar
  24. MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992b)CrossRefMATHGoogle Scholar
  25. Maier, H.R., Dandy, G.C.: Neural networks for the prediction and forecasting of water resources variables : a review of modelling issues and applications 15, 101–124 (2000)Google Scholar
  26. Maqsood, I., Khan, M., Abraham, A.: An ensemble of neural networks for weather forecasting. Neural Comput. Appl. 13, 112–122 (2004)CrossRefGoogle Scholar
  27. Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)CrossRefGoogle Scholar
  28. Moré, J.J.: The levenberg-marquardt algorithm: implementation and theory. In: Lecture Notes in Mathematics, pp. 105–116. Springer (2015)Google Scholar
  29. Nor, M., Rakhecha, P.: Analysis of a severe tropical urban storm in Kuala Lumpur, Malaysia. In: 11th International Conference on Urban Drainage, pp. 1–9 (2008)Google Scholar
  30. Okut, H., Wu, X.-L., Rosa, G.J.M., Bauck, S., Woodward, B.W., Schnabel, R.D., Gianola, D.: Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. Genet. Sel. Evol.: GSE 45(1), 34 (2013)Google Scholar
  31. 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. Inform. Technol. 77(2), 287–290 (2015)Google Scholar
  32. Reed, R.D., Marks, R.J.: Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. Mit Press (1998)Google Scholar
  33. Rezaeianzadeh, M., Tabari, H., Arabi Yazdi, A., Isik, S., Kalin, L.: Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput. Appl. 14–16 (2013)Google Scholar
  34. Sabit, H., Al-Anbuky, A.: Multivariate spatial condition mapping using subtractive fuzzy cluster means. Sensors (Switzerland) 14(10), 18960–18981 (2014)CrossRefGoogle Scholar
  35. Shahi, A.: An effective fuzzy c-mean and type-2 fuzzy. J. Theor. Appl. Inf. Technol. 556–567 (2009)Google Scholar
  36. Shamseldin, A.Y.: Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199(3–4), 272–294 (1997)CrossRefGoogle Scholar
  37. Sheela, K.G., Deepa, S.N.: Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng. (2013)Google Scholar
  38. Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. Science 49(CS-94–125), 64 (1994)Google Scholar
  39. Srivastava, G., Panda, S.N., Mondal, P., Liu, J.: Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique. J. Hydrol. 395(3–4) (2010)Google Scholar
  40. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetMATHGoogle Scholar
  41. Sugumar, R., Rengarajan, A., Jayakumar, C.: A technique to stock market prediction using fuzzy clustering and artificial neural networks. Comput. Inf. 33(5), 992–1024 (2015)Google Scholar
  42. Toth, E., Brath, A., Montanari, A.: Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239(1–4), 132–147 (2000)Google Scholar
  43. Vega-corona, A.: ANN and Fuzzy c-Means Applied to Environmental Pollution Prediction, 1–6 (2012)Google Scholar
  44. 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
  • 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