Estimation of Reservoir Discharge with the Help of Clustered Neurogenetic Algorithm

  • Mrinmoy MajumderEmail author
  • Rabindra Nath Barman
  • Pankaj Roy
  • Bipal K. Jana
  • Asis Mazumdar


This chapter presents a new approach of reservoir out flow prediction using a clustered neurogenetic algorithm. The algorithm combines the learning ability of artificial neural networks with searching capability of the genetic algorithm. The model is tested on the Panchet reservoir in river Damodar using the historical, hydrological, and water supply dataset. The values of the input parameters are classified into six groups based on the magnitude of the input parameters. The results showed a highly adaptive and flexible investigating ability of the model in prediction of nonlinear relationships among different variables.


Classified neurogenetic models discharge model performance multi-reservoirs 



The authors would like to acknowledge Dr. Chandan Ray, Retd. Chief Engineer, Irrigation and Drainage Department, West Bengal Govt. and Dr. Debasri Roy, Joint Coordinator, School of Water Resources Engineering, Jadavpur University, West Bengal, India for their valued comments and reviews, which helped in the preparation of the chapter.


  1. Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. J Water Resour Manage 19:145–161CrossRefGoogle Scholar
  2. Anctil F, Rat A (2005) Evaluation of neural network stream flow forecasting on 47 watersheds. J Hydrol Eng 10(1):85–88CrossRefGoogle Scholar
  3. ASCE Task Committee (2000) Application of artificial neural networks in hydrology, artificial neural networks in hydrology I: preliminary concepts. J Hydrol Eng 5(2):115–123CrossRefGoogle Scholar
  4. Bhatt VK, Bhattacharya P, Tiwari AK (2007) Application of artificial neural network in estimation of rainfall erosivity. Hydrol J 1–2:30–39Google Scholar
  5. Cigizoglu HK (2005) Application of the generalized regression neural networks to intermittent flow forecasting and estimation. ASCE J Hydrol Eng 10(4):336–341CrossRefGoogle Scholar
  6. Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Software 37(2):63–68CrossRefGoogle Scholar
  7. Clair TA, Ehrman JM (1998) Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resour Res 34(3):447–455CrossRefGoogle Scholar
  8. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257CrossRefGoogle Scholar
  9. Coulibaly P, Haché M, Fortin V, Bobée B (2005) Improving daily reservoir inflow forecasts with model combination. J Hydrol Eng 10(2):91–99CrossRefGoogle Scholar
  10. Deka P, Chandramouli V (2005) Fuzzy neural network model for hydrologic flow routing. J Hydrol Eng 10(4):302–314CrossRefGoogle Scholar
  11. Elshorbagy A, Simonovic SP, Panu US (2000) Performance evaluation of artificial neural networks for runoff prediction. J Hydrol Eng 5(4):424–427CrossRefGoogle Scholar
  12. Fernando DA, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209CrossRefGoogle Scholar
  13. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, London, p 1Google Scholar
  14. Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):251–253CrossRefGoogle Scholar
  15. Imrie CE, Durucan S, Korre A (2000) River flow prediction using neural networks: generalization beyond the calibration range. J Hydrol 233(3–4):138–154CrossRefGoogle Scholar
  16. Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manage 125(5):263–271CrossRefGoogle Scholar
  17. Karaboga D, Bagis A, Haktanir T (2004) Fuzzy logic based operation of spillway gates of reservoirs during floods. J Hydrol Eng 9(6):544–549CrossRefGoogle Scholar
  18. Kisi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  19. Lahiri-Dutt K (2000) State and the community in water management case of the Damodar Valley Corporation, India. Report on Resource Management in Asia Pacific Program, The Australian National UniversityGoogle Scholar
  20. Liong SY, Khu ST, Chan WT (2001) Derivation of Pareto front with genetic algorithm and neural network. J Hydrol Eng 6(1):52–61CrossRefGoogle Scholar
  21. Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting. Water Resour Res 35(8):2591–2596CrossRefGoogle Scholar
  22. Majumder M, Roy P, Mazumdar A (2007) Optimization of the water use in the river Damodar In West Bengal In India: an integrated multi-reservoir system with the help of artificial neural network. J Eng Comput Architect 1(2): Article no.1192Google Scholar
  23. Neelakantan TR, Pundarikanthan NV (2000) Neural network based simulation-optimization model for reservoir operation. J Water Resour Plan Manage 126(2):57–64CrossRefGoogle Scholar
  24. Parasuraman K, Elshorbagy A (2007) Cluster-based hydrologic prediction using genetic algorithm-trained neural networks. J Hydrol Eng ASCE 12:52–62CrossRefGoogle Scholar
  25. Ray C, Klindworth KK (2000) Neural networks for agrichemical vulnerability assessment of rural private wells. J Hydrol Eng 5(2):162–171CrossRefGoogle Scholar
  26. Singh VP (1995) Computer models of watershed hydrology. Water Resource Publications, Highlands Ranch, COGoogle Scholar
  27. Singh VP, Woolhiser DA (2002) Mathematical modeling of watershed hydrology. J Hydrol Eng 7(4):270–292CrossRefGoogle Scholar
  28. Sudheer KP (2005) Knowledge extraction from trained neural network river flow models. J Hydrol Eng 10(4):L264–269CrossRefGoogle Scholar
  29. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng 4(3):232–239CrossRefGoogle Scholar
  30. Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour Res 27(9):2467–2471CrossRefGoogle Scholar
  31. Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plan Manage 125(1):25–33CrossRefGoogle Scholar
  32. World Metereological Organization (WMO) (1992) Simulated real-time intercomparison of hydrological models. Retrieved from on 25th June 2008
  33. Yitian L, Gu RR (2003) Modeling flow and sediment transport in a river system using an artificial neural network. J Environ Manage 31(1):122–134CrossRefGoogle Scholar
  34. Zhang Q, Stanley SJ (1999) Real-time treatment process control with artificial neural networks. J Environ Eng 125(2):153–160CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Mrinmoy Majumder
    • 1
    • 2
    Email author
  • Rabindra Nath Barman
    • 1
    • 3
  • Pankaj Roy
    • 1
  • Bipal K. Jana
    • 1
    • 4
  • Asis Mazumdar
    • 1
    • 2
  1. 1.School of Water Resources EngineeringJadavpur UniversityKolkataIndia
  2. 2.Regional Center, National Afforestation and Eco-development BoardJadavpur UniversityKolkataIndia
  3. 3.Department of ProductionNational Institute of TechnologyAgartalaIndia
  4. 4.Consulting Engineering ServicesWest BengalIndia

Personalised recommendations