Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

  • Mohammad Babaei
  • Ramtin MoeiniEmail author
  • Eghbal Ehsanzadeh


Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.


Inflow discharge prediction Artificial neural network Support vector machine Input data pattern Zayandehroud dam 


Compliance with Ethical Standards

Conflict of Interest



  1. Asefa T, Kemblowski MW, Urroz G, McKee M, Khalil A (2004) Support vectors-based groundwater head observation networks design. Water Resour Res 40(11):W11509CrossRefGoogle Scholar
  2. Asefa T, Kemblowski MW, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318:7–16CrossRefGoogle Scholar
  3. Awchi TA (2014) River discharges forecasting in northern Iraq using different ANN techniques. Water Resour Manag 28(3):801–814CrossRefGoogle Scholar
  4. Coulibali CG (1999) Daily stream flow forecasting: application of ANN. J Hydrol 3:123–128Google Scholar
  5. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3):244–257CrossRefGoogle Scholar
  6. Dibike YB, Yelickov S, Solomatine DP, Abbott MB (2001) Model induction with support vector machines: introduction and application. Journal of Computing in Civil Engineering Management 15(3):208–216CrossRefGoogle Scholar
  7. Hassan M, Shamim MA, Hashmi HN, Ashiq SZ, Ahmed I, Pasha GA, Naeem UA, Ghumman AR, Han D (2015) Predicting streamflows to amultipurpose reservoir using artificial neural networks and regression techniques. Earth Sci Inf 8(2):337–352CrossRefGoogle Scholar
  8. He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386CrossRefGoogle Scholar
  9. Jamali S, Abrishamchi A, Tajrishi M (2007) River stream-flow and Zayanderoud reservoir operation modeling using the fuzzy inference system. Journal of Water and Wastewater 18(4):25–34 (in Persian)Google Scholar
  10. Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manag 29(4):1283–1293CrossRefGoogle Scholar
  11. Kisi O (2008) River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res 39(1):27–40CrossRefGoogle Scholar
  12. Kisi O, Cimen M (2011) A wavelet–support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140CrossRefGoogle Scholar
  13. Lima LM, Popova E, Damien P (2014) Modeling and forecasting of Brazilian reservoir inflows via dynamic linear models. Int J Forecast 30(3):464–476CrossRefGoogle Scholar
  14. Lin J-Y, Cheng C-T, Chau K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51(4):599–612CrossRefGoogle Scholar
  15. Liong S, Sivapragasam C (2002) Flood stage forecasting with support vector machines. J Am Water Resour Assoc 38:173–186CrossRefGoogle Scholar
  16. Liu Z, Zhou P, Zhang Y (2014) A probabilistic wavelet–support vector regression model for stream flow forecasting with rainfall and climate information input. J Hydrometeorol 16:2209–2229CrossRefGoogle Scholar
  17. Liu J, Yan K, Zhao X, Hu Y (2016) Prediction of autogenous shrinkage of concretes by support vector machine. International Journal of Pavement Research and Technology 9(3):169–177CrossRefGoogle Scholar
  18. Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442-443:23–35CrossRefGoogle Scholar
  19. Menhaj MB (2009) Computational intelligence. AmirKabir University of Technology (in persian)Google Scholar
  20. Noori R, Khakpour AR, Omidvar B, Farokhnia A (2010) Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Syst Appl 37(8):5856–5862CrossRefGoogle Scholar
  21. Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Ghafari Gousheh M (2011a) Assessment of input variables determination on the SVM model performance using PCA, gamma test and forward selection techniques for monthly stream flow prediction. J Hydrol 401:177–189CrossRefGoogle Scholar
  22. Noori R, Karbassi AR, Mehdizadeh HM, Vesali-Naseh M, Sabahi MS (2011b) A framework development for predicting the longitudinal dispersion coefficient in natural streams using an artificial neural network. Environ Prog Sustain Energy 30(3):439–449CrossRefGoogle Scholar
  23. Sattari MT, Yurekli K, Pal M (2012) Performance evaluation of artificial neural network approaches in forecasting reservoir inflow. Appl Math Model 36(6):2649–2657CrossRefGoogle Scholar
  24. Smola AJ, Scholkopf B (1998) A tutorial on support vector regression. NeuroCOLT2 Technical Report Series. NC2-TR-1998-030Google Scholar
  25. Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural. J Hydrol 476:433–441CrossRefGoogle Scholar
  26. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New YorkCrossRefGoogle Scholar
  27. Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  28. Wenjian W, Changqian M, Weizhen L (2008) Online prediction model based on support vector machine. Neurocomputing 71(5):550–558Google Scholar
  29. Yazdani MR, Saghafian B, Mahdian MH, Soltani S (2009) Monthly runoff estimation using artificial neural networks. J Agric Sci Technol 11(3):355–362Google Scholar
  30. Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328:704–716CrossRefGoogle Scholar
  31. ZayandAb Consulting Engineers (2008) Studies on resources and water consumption for Zayandehroud Basin. Isfahan Regional Water CompanyGoogle Scholar
  32. Zhu S, Zhou J, Ye L, Meng C (2016) Stream flow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China. Environ Earth Sci 75(531):1–12Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Mohammad Babaei
    • 1
  • Ramtin Moeini
    • 1
    Email author
  • Eghbal Ehsanzadeh
    • 2
  1. 1.Department of Civil Engineering, Faculty of Civil Engineering and TransportationUniversity of IsfahanIsfahanIran
  2. 2.Department of Water Engineering, Faculty of AgricultureIlam UniversityIlamIran

Personalised recommendations