Skip to main content
SpringerLink
Log in

A hybrid SVM-PSO model for forecasting monthly streamflow

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The long-term streamflow forecasts are very significant in planing and reservoir operations. The streamflow forecasts have to deal with a complex and highly nonlinear data patterns. This study employs support vector machines (SVMs) in predicting monthly streamflows. SVMs are proved to be a good tool for forecasting the nonlinear time series. But the performance of the SVM depends solely upon the appropriate choice of parameters. Hence, particle swarm optimization technique is employed in tuning SVM parameters. The proposed SVM-PSO model is used in forecasting the streamflow values of Swan River near Bigfork and St. Regis River near Clark Fork of Montana, United States. Further SVM model with various input structures is constructed, and the best structure is determined using various statistical performances. Later, the performance of the SVM model is compared with the autoregressive moving average model (ARMA) and artificial neural networks (ANN's). The results indicate that SVM could be a better alternative for predicting monthly streamflows as it provides a high degree of accuracy and reliability.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Huang Z, Chau K (2008) A new image thresholding method based on gaussian mixture model. Appl Math Comput 205(2):899–907

    Article  MATH  MathSciNet  Google Scholar 

  2. Cheng C, Ou C, Chau K (2002) Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. J Hydrol 268(1):72–86

    Article  Google Scholar 

  3. Xie J, Cheng C, Chau K, Pei Y (2006) A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity. Int J Environ Pollut 28(3):364–381

    Article  Google Scholar 

  4. Jia W, Ling B, Chau K, Heutte L (2008) Palmprint identification using restricted fusion. Appl Math Comput 205(2):927–934

    Article  MATH  MathSciNet  Google Scholar 

  5. Sivapragasam C, Liong S, Pasha M (2001) Rainfall and runoff forecasting with SSA-SVM approach. J Hydroinformat 3(3):141–152

    Google Scholar 

  6. Yu X, Liong S, Babovic V (2002) Hydrologic forecasting with support vector machine combined with chaos-inspired approach. In: Hydroinformatics 2002: Proceedings of the 5th international conference on hydroinformatics, pp. 1–5

  7. Khadam I, Kaluarachchi J (2004) Use of soft information to describe the relative uncertainty of calibration data in hydrologic models. Water Resour Res 40(11):W11505

    Google Scholar 

  8. Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318(1–4):7–16

    Article  Google Scholar 

  9. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126

    Article  MATH  Google Scholar 

  10. Müller K, Smola A, Rätsch G, Schölkopf B, Kohlmorgen J, Vapnik V (2000) Using support vector machines for time series prediction. In: Advances in kernel methods: support vector machine. MIT press, Cambridge

  11. Schölkopf B, Bartlett P, Smola A, Williamson R Support vector regression with automatic accuracy control. In: Proceedings of ICANN’98, perspectives in neural computing

  12. Dibike Y, Velickov S, Solomatine D, Abbott M (2001) Model induction with support vector machines: introduction and applications. J Comput Civil Eng 15(3):208–216

    Article  Google Scholar 

  13. Da Wei H, Cluckie I (2004) Support vector machines identification for runoff modelling. In: Proceedings of the 6th international conference on hydroinformatics: Singapore, 21–24 June 2004, World Scientific, p. 1597

  14. Sivapragasam C, Liong S, Pasha M (2001) Rainfall and runoff forecasting with SSA-SVM approach. J Hydroinformatics 3(3):141–152

    Google Scholar 

  15. Choy K, Chan C (2003) Modelling of river discharges and rainfall using radial basis function networks based on support vector regression. Int J Syst Sci 34(1):763–773

    Article  MATH  MathSciNet  Google Scholar 

  16. Yu X, Liong S, Babovic V (2004) EC-SVM approach for real-time hydrologic forecasting. J Hydroinformatics 6(3):209–223

    MATH  Google Scholar 

  17. Yu P, Chen S, Chang I (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3-4):704–716

    Article  Google Scholar 

  18. Samal N, Konar A, Das S, Abraham A (2007) A closed loop stability analysis and parameter selection of the particle swarm optimization dynamics for faster convergence. In: Evolutionary computation, 2007. CEC 2007. IEEE congress on, IEEE, pp. 1769–1776

  19. Ghosh S, Das S, Kundu D, Suresh K, Panigrahi B, Cui Z (2012) An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization. Neural Comput Appl 21(2):237–250

    Article  Google Scholar 

  20. Banerjee T, Das S (2012) Multi-sensor data fusion using support vector machine for motor fault detection. Inf Sci 217:96–107

    Article  Google Scholar 

  21. Nasir M, Das S, Maity D, Sengupta S, Haldar U, Suganthan P (2012) A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf Sci 209:16–36

    Article  Google Scholar 

  22. Deep K, Bansal J (2009) Hybridization of particle swarm optimization with quadratic approximation. Opsearch 46(1):3–24

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India

    Ch. Sudheer, R. Maheswaran & Shashi Mathur

  2. Department of Electrical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India

    B. K. Panigrahi

Authors
  1. Ch. Sudheer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Maheswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. K. Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shashi Mathur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ch. Sudheer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sudheer, C., Maheswaran, R., Panigrahi, B.K. et al. A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput & Applic 24, 1381–1389 (2014). https://doi.org/10.1007/s00521-013-1341-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1341-y

Keyword

Search

Navigation