Skip to main content
SpringerLink
Log in

Improving the Predictability of GRNN Using Fruit Fly Optimization and PCA: The Nile Flood Forecasting

  • Conference paper
  • First Online:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

  • 2503 Accesses

Abstract

Generalized regression neural network (GRNN) is commonly used for function approximation. The predictability of GRNN for Nile flood forecasting is investigated through optimization of the smoothing parameter using fruit-fly (FOA) algorithm. Due to the excess number of inputs that are highly correlated, principal component analysis (PCA) is used to reduce the dimension of a set of linearly correlated variables to improve forecasting task. Our empirical experiment shows that the performance of GRNN is better than other neural network included in this paper. The results of the prediction made by the proposed model showed a close fit with the actual data.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis Forecasting and Control, 5th edn. Wiley, New York (2016)

    MATH  Google Scholar 

  2. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, Upper Saddle River (2009)

    Google Scholar 

  3. Atiya, A., El-Shoura, S., Shaheen, S., El-Sherif, M.: A Comparison between neural-network forecasting techniques case study: river flow forecasting. IEEE Trans. Neural Netw. 10(2), 402–409 (1999)

    Article  Google Scholar 

  4. Yu, X.: Can back-propagation error surface not have local minima? IEEE Trans. Neural Netw. 45, 1019–1021 (1992)

    Article  Google Scholar 

  5. Specht, D.: A general regression neural network. IEEE Trans. Neural Netw. 2, 568–576 (1991)

    Article  Google Scholar 

  6. Kennedy, J., Eberhart, R., R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  7. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). doi:10.1007/BFb0040810

    Chapter  Google Scholar 

  8. Dorigo, M., Sttzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    MATH  Google Scholar 

  9. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical report-TR06, Erciyes University, Engineering Faculty, Department of Computer Engineering (2005)

    Google Scholar 

  10. Yang, X., Deb, S.: Cuckoo search via Levy Flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC2009), pp. 210–214 (2009)

    Google Scholar 

  11. Geem, Z., Kim, J., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68 (2011)

    Article  Google Scholar 

  12. Li, W., Wang, H., Zou, Z., Qian, J.: Function optimization method based on bacterial colony chemotaxis. J. Circ. Syst. 10, 58–63 (2005)

    Google Scholar 

  13. Pan, W.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl. Based Syst. 26, 69–74 (2011)

    Article  Google Scholar 

  14. Pan, W.: A new evolutionary computation approach: fruit fly optimization algorithm. In: Conference of Digital Technology and Innovation Management (2011)

    Google Scholar 

  15. Xing, B., Gao, W.: Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. Intelligent Systems Reference Library. Springer, Heidelberg (2014)

    Book  MATH  Google Scholar 

  16. Yang, X.: A nature-Inspired metaheuristic bat-inspired algorithms. Stud. Comput. Intell. 284, 65–74 (2010)

    MATH  Google Scholar 

  17. Li, H., Guo, S., Li, C., Sun, J.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl. Based Syst. 37, 378–387 (2013)

    Article  Google Scholar 

  18. Sun, W., Ye, M.: Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by fruit fly optimization algorithm. J. Electr. Comput. Eng., pp. 2–9 (2015)

    Google Scholar 

  19. Ren, S., Yang, D., Ji, F., Tian, X.: Application of generalized regression neural network in prediction of cement properties. In: International Conference on Computer Design and Applications (2010)

    Google Scholar 

  20. Wang, Z., Sheng, H.: Rainfall prediction using generalized regression neural network: case study Zhengzhou. In: International Conference on Computational and Information Sciences (2010)

    Google Scholar 

  21. Al-Daoud, E.: A comparison between three neural network models for classification problems. J. Artif. Intell. 2, 56–64 (2009)

    Article  Google Scholar 

  22. Ozyildirim, B., Avci, M.: Generalized classifier neural network. Neural Netw. 39, 18–26 (2013)

    Article  MATH  Google Scholar 

  23. Zhao, S., Zhang, J., Li, W., Song, W.: A generalized regression neural network based on fuzzy means clustering and its application in system identification. In: International Symposium on Information Technology Convergence (2007)

    Google Scholar 

  24. Erkmen, B., Yildirim, T.: Improving classification performance of sonar targets by applying general regression neural network with PCA. Expert Syst. Appl. 35, 472–475 (2008)

    Article  Google Scholar 

  25. Leung, M., Chen, A., Daouk, H.: Forecasting exchange rates using general regression neural networks. Comput. Oper. Res. 27, 1093–1110 (2000)

    Article  MATH  Google Scholar 

  26. Lin, S.: Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network. Neural Comput. Appl. 22, 783–791 (2013)

    Article  Google Scholar 

  27. Burges, C.: Dimension reduction: a guided tour. Found. Trend Mach. Learn. 2(4), 275–365 (2009)

    Article  MATH  Google Scholar 

  28. El-Telbany, M., Abdelwhab, A., Shaheen, S.: Forecasting of the nile river inflow using genetic algorithms. In: Proceedings of International Conference on ANN and GA (1997)

    Google Scholar 

  29. El-Telbany, M., Konsowa, H., El-Addawi, M., M.: Studying the predictability of neural network learned by particle swarm optimization. Egypt. J. Eng. Appl. Sci., pp. 377–390 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computers and Systems Department, Electronics Research Institute, Giza, Egypt

    Mohammed E. El-Telbany

Authors
  1. Mohammed E. El-Telbany
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed E. El-Telbany .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

El-Telbany, M.E. (2017). Improving the Predictability of GRNN Using Fruit Fly Optimization and PCA: The Nile Flood Forecasting. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48308-5_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation