Skip to main content
SpringerLink
Log in

Modeling Meteorological Prediction Using Particle Swarm Optimization and Neural Network Ensemble

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Included in the following conference series:

Abstract

In this paper a novel optimization approach is presented. Network architecture and connection weights of neural networks (NN) are evolved by a particle swarm optimization (PSO) method, and then the appropriate network architecture and connection weights are fed into back-propagation (BP) networks. The ensemble strategy is carried out by simple averaging. The applied example is built with monthly mean rainfall of the whole area in Guangxi, China. The results show that the proposed approach can effectively improves convergence speed and generalization ability of NN.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dean, A.R., Brian, H.F.: Forecasting Warm-season Burn-off Low Clouds at the San Francisco International Airport Using Linear Regression and a Neural Network. Applied Meteor 41(6), 629–639 (2002)

    Article  Google Scholar 

  2. Hsieh, W.W.: Nonlinear Canonical Correlation Analysis of the Tropical Pacific Climate Variability Using Neural Network Approach. Journal of Climate 14(12), 2528–2539 (2001)

    Article  Google Scholar 

  3. Jin, L., Ju, W., Miao, Q.: Study on Ann-based Multi-step Prediction Model of Short-term Climatic Variation. Advances Atmosphere Sciences 17(1), 157–164 (2000)

    Article  Google Scholar 

  4. Jin, L., Kuang, X.: Study on the Over-fitting of the Artificial Neural Network Forecasting Model. Acta Meteorologica Sinica 62(1), 62–69 (2004)

    Google Scholar 

  5. Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)

    Article  Google Scholar 

  6. Sollich, P., Krogh, A.: Learning with Ensembles: How Over-fitting Can Be Useful. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, Denver, CO, vol. 8, pp. 190–196. MIT Press, Cambridge (1996)

    Google Scholar 

  7. Gutta, S., Wechsler, H.: Face Recognition Using Hybrid Classifier Systems. In: Processing ICNN 1996, Washington, DC, pp. 1017–1022 (1996)

    Google Scholar 

  8. Mao, J.: A Case Study on Bagging Boosting and Basic Ensembles of Neural Networks for OCR. In: Processing IJCNN 1998, Anchorage, AK, vol. 3, pp. 1828–1833 (1998)

    Google Scholar 

  9. Sollich, P., Intrator, N.: Classification of Seismic Signals by Integrating Ensembles of Neural Networks. IEEE Transactions Signal Processing 46(5), 1194–1021 (1998)

    Google Scholar 

  10. Li, N., Zhou, H., Ling, J.: Speculated Lesion Detection in Digital Mammogram Based on Artificial Neural Network Ensemble. In: Advances in Neural Networks ISNN, pp. 790–795. Springer Press, Heidelberg (2005)(III)

    Google Scholar 

  11. Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for Optimization from Social Insect Behavior. Nature 406(6), 39–42 (2000)

    Article  Google Scholar 

  12. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  13. Rumlhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back Propagating Errors. Nature 323(11), 533–536 (1986)

    Article  Google Scholar 

  14. Reed, R.: Pruning Algorithms-A Survey. IEEE Transactions on Neural Networks 4, 740–747 (1993)

    Article  Google Scholar 

  15. Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computation Abilities. In: Proceedings of the National Academy of Science, pp. 2554–2558 (1982)

    Google Scholar 

  16. 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, Alaska, USA (1998)

    Google Scholar 

  17. Vautard, S.A.: A Toolkit for Noisy Chaotic Signals. Physical D 58, 95–126 (1992)

    Article  Google Scholar 

  18. Wei, F., Cao, H.: The Mathematics Forecast Model and Application of Long Period Time, pp. 258–365. The Meteorological Press, Beijing (1990)

    Google Scholar 

  19. Wang, H.: The Model and Application of Partial Least-Squares Regression. In: The National Defense Science and Technology, pp. 47–56. University Press, China (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer, Liuzhou Teachers College, Guangxi, China

    Jiansheng Wu

  2. Guangxi Research Institute of Meteorological Disaster Mitigation, Nanning, Guangxi, China

    Long Jin

  3. Institutes of Information Sciences and Technology, Massey University, Private Bag 11222, Palmerston North, New Zealand

    Mingzhe Liu

Authors
  1. Jiansheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Long Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingzhe Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, J., Jin, L., Liu, M. (2006). Modeling Meteorological Prediction Using Particle Swarm Optimization and Neural Network Ensemble. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_175

Download citation

  • DOI: https://doi.org/10.1007/11760191_175

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation