Short-Term Localized Weather Forecasting by Using Different Artificial Neural Network Algorithm in Tropical Climate

  • Noor Zuraidin Mohd-SafarEmail author
  • David Ndzi
  • Ioannis Kagalidis
  • Yanyang Yang
  • Ammar Zakaria
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from North-West Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best.


Artificial neural network ANN Short-term weather forecasting Neural network Soft computing Tropics Tropical climate 


  1. 1.
    Ndzi, D.L., Harun, A., Ramli, F.M., Kamarudin, M.L., Zakaria, A., Shakaff, A.Y.M., Jaafar, M.N., Zhou, S., Farook, R.S.: Wireless sensor network coverage measurement and planning in mixed crop farming. Comput. Electron. Agric. 105, 83–94 (2014)CrossRefGoogle Scholar
  2. 2.
    Rahul, G.K., Khurana, M.: A comparative study review of soft computing approach in weather forecasting. Int. J. Soft Comput. Eng. (IJSCE), 2(5), 295–299 (2012)Google Scholar
  3. 3.
    Cheng, B., Titterington, D.M.: Neural networks: a review from a statistical perspective. Stat. Sci. 9(1), 2–30 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Nikam, V.B., Meshram, B.B.: Modeling rainfall prediction using data mining method: a Bayesian approach. In: 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, pp. 132–136 (2013)Google Scholar
  5. 5.
    Shahi, A.: An effective fuzzy C-Mean and Type-2 Fuzzy. J. Theor. Appl. Inf. Technol. 5(5), 556–567 (2009)Google Scholar
  6. 6.
    Maqsood, I., Khan, M., Abraham, A.: An ensemble of neural networks for weather forecasting. Neural Comput. Appl. 13, 112–122 (2004)CrossRefGoogle Scholar
  7. 7.
    Paras, S.M., Kumar, A., Chandra, M.: A feature based neural network model for weather forecasting. Int. J. Comput. Intell. 4(3) (2009)Google Scholar
  8. 8.
    Hayati, M., Mohebi, Z.: Application of artificial neural networks for temperature forecasting. World Acad. Sci. Eng. Technol. 28(2), 275–279 (2007)Google Scholar
  9. 9.
    Hossain, M., Rekabdar, B., Louis, S.J., Dascalu, S.: Forecasting the weather of Nevada: a deep learning approach. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2015, pp. 2–7, September 2015Google Scholar
  10. 10.
    LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.R.: Efficient backprop. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS, vol. 7700, pp. 9–48 (2012)Google Scholar
  11. 11.
    Abraham, A., Philip, N.S., Joseph, K.B.: Will We Have a Wet Summer ? Soft Computing Models for Long-term Rainfall Forecasting (1992)Google Scholar
  12. 12.
    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
  13. 13.
    Pellakuri, V., Rao, D.R., Prasanna, P.L., 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
  14. 14.
    Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Lecture Notes in Mathematics, Springer, pp. 105–116 (1978)Google Scholar
  15. 15.
    MacKay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448–472 (1992)CrossRefGoogle Scholar
  16. 16.
    MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)CrossRefzbMATHGoogle Scholar
  17. 17.
    Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. Science 49(CS-94–125), 64 (1994)Google Scholar
  18. 18.
    Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)CrossRefGoogle Scholar
  19. 19.
    Schneider, T.: Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J. Clim. 14, 853–871 (2001)CrossRefGoogle Scholar
  20. 20.
    Josse, J., Pages, J., Husson, F.: Multiple imputation in principal component analysis. Adv. Data Anal. Classif. 5(3), 231–246 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Manly, B.: Statistics for Environmental Science and Management. Chapman and Hall/CRCM, Boca Raton (2000)CrossRefzbMATHGoogle Scholar
  22. 22.
    Howard Demuth, M.B., Hagan, M.: Neural network toolbox user’s guide’ (2012)Google Scholar
  23. 23.
    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

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Noor Zuraidin Mohd-Safar
    • 1
    Email author
  • David Ndzi
    • 1
  • Ioannis Kagalidis
    • 1
  • Yanyang Yang
    • 1
  • Ammar Zakaria
    • 2
  1. 1.School of EngineeringUniversity of PortsmouthPortsmouthUK
  2. 2.School of Mechatronic EngineeringUniversiti Malaysia PerlisArauMalaysia

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