A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network

  • Jiansheng Wu
  • Enhong Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5553)


In this study, we propose a novel nonparametric regression (NR) ensemble rainfall forecasting model integrating generalized particle swarm optimization (PSO) with artificial neural network (ANN). First of all, the PSO algorithm is used to evolve neural network architecture and connection weights. The evolved neural network architecture and connection weights are input into a new neural network.The new neural network is trained using back-propagation (BP) algorithm, generating different individual neural network. Then, the principal component analysis (PCA) technology is adopted to extract ensemble members. Finally, the NR is used for nonlinear ensemble model. Empirical results obtained reveal that the prediction by using the NR ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. For illustration and testing reveal that the NR ensemble model proposed can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.


Nonparametric Regression Neural Network Ensemble Particle Swarm Optimization Rainfall Forecasting 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hong, W.C.: Rainfall Forecasting by Technological Machine Learning Models. Applied Mathematics and Computation 200, 41–47 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Luk, K.C., Ball, J.E., Sharma, A.: An Application of Artificial Neural Networks for Rainfall Forecasting. Mathematical and Computer Modelling 33, 683–693 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Nasseri, M., Asghari, K., Abedini, M.J.: Optimized Scenario for Rainfall Forecasting Using Genetic Algorithm Coupled with Artificial Neural Network. Expert Systems with Application 35, 1414–1421 (2008)CrossRefGoogle Scholar
  4. 4.
    Hsieh, W., Tang, B.: Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. Bull. Am. Meteorol, Soc. 79, 1855–1870 (1998)CrossRefGoogle Scholar
  5. 5.
    Valverde, M.C., Campos Velho, H.F., Ferreira, N.J.: Artificial Neural Network Technique for Rainfall Forecasting Applied to the Sö Paulo Region. Journal of Hydrology 301(1-4), 146–162 (2005)CrossRefGoogle Scholar
  6. 6.
    Wang, W., Xu, Z., Lu, J.W.: Three Improved Neural Network Models for Air Quality Forecasting. Engineering Computations 20(2), 192–210 (2003)CrossRefzbMATHGoogle Scholar
  7. 7.
    Hykin, S.: Neural Networks: A Comprehensive Foundation. Printice-Hall, Inc., New Jersey (1999)Google Scholar
  8. 8.
    Lin, G.F., Chen, L.H.: Application of an Artificial Neural Network to Typhoon Rainfall Forecasting. Hydrological Processes 19, 1825–1837 (2005)CrossRefGoogle Scholar
  9. 9.
    Wu, J.S., Jin, L.: Forecast Research and Applying of BP Neural Network Based on Genetic Algorithms. Mathematics in Practice and Theory 35(1), 83–88 (2005)Google Scholar
  10. 10.
    Wu, J.S., Jin, L., Liu, M.Z.: Modeling Meteorological Prediction Using Particle Swarm Optimization and Neural Network Ensemble. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1202–1209. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Yu, L., Wang, S., Lai, K.K.: A Novel Nonlinear Ensemble Forecasting Model Incorporating Glar Andann for Foreign Exchange Rates. Computers Operations Research 32, 2523–2541 (2005)CrossRefzbMATHGoogle Scholar
  12. 12.
    Brandstatter, B., Baumgartner, U.: Particle Swarm Optimization-Mass-Spring System Analogon. IEEE Transactions on Magnetics 38, 997–1000 (2002)CrossRefGoogle Scholar
  13. 13.
    Fan, S.K., Liang, Y.C.: Hybrid Simplex Search and Particle Swarm Optimization for the Global Optimization of Multimodal Functions. Engineering Optimization 36, 401–418 (2003)CrossRefGoogle Scholar
  14. 14.
    Kennedy, J., Spears, W.: Matching Algorithms to Problems: an Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimode Problem Generator. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA (1998)Google Scholar
  15. 15.
    Nason, G.P., Silverman, B.W.: Wavelets for Regression and Other Statistical Problems. In: Schimek, M.G. (ed.) Smoothing and Regression: Approaches, Computation, and Application. Wiley, New York (2000)Google Scholar
  16. 16.
    Fox, J.: Multiple and Generalized Nonparametric Regression. Sage, Thousand Oaks (2000)CrossRefzbMATHGoogle Scholar
  17. 17.
    Ioannides, D.A., Alevizos, P.D.: Nonparametric Regression with Errors in Variables and Application. Statistics & Probability Letters 32, 35–43 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Shen, H.P., Lawrence, D.B.: Nonparametric Modelling of Time-Varying Customer Service Times at Bank Call Centre. Applied Stochastic Models in Business and Industry 22, 297–311 (2006)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jiansheng Wu
    • 1
  • Enhong Chen
    • 2
  1. 1.Department of Mathematics and ComputerLiuzhou Teacher CollegeGuangxiChina
  2. 2.Department of ComputerUniversity of Science and Technology of ChinaHefeiChina

Personalised recommendations