Advertisement

Regime-Wise Genetic Programming Model for Improved Streamflow Forecasting

  • K. Bhavita
  • D. Swathi
  • J. Manideep
  • D. Sree Sandeep
  • Maheswaran RathinasamyEmail author
Conference paper
  • 470 Downloads

Abstract

Forecasting of stream flow plays a vital role in flood forecasting studies, design, and operation of reservoirs. Several approaches such as physical models, conceptual models and statistical/black-box models are used to model complex uncertain peak flows in rivers. In the past, Genetic Programming (GP) have been a widely used for different hydrological applications. In this study we propose a regime-wise genetic programming model for efficient forecasting of streamflow during peak flows. In this approach, we first classify the flows into three regimes such as low, med and high based on their flow magnitude and develop separate GP models. The proposed approach was applied to a case study from Godavari River Basin, India. The results obtained show that the proposed approach of separate models for high flows performs better than the single model for all regimes.

Keywords

Genetic programming Streamflow forecasting Flow regime 

References

  1. 1.
    Rabunal, J.R., Puertas, J., Rivero, D.: Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol. Process. 21(4), 476–485 (2006)CrossRefGoogle Scholar
  2. 2.
    Selle, B., Mutil, N.: Testing the structure of a hydrological model using genetic programming. J. Hydrol. 397(1–2), 1–9 (2010)Google Scholar
  3. 3.
    Parasuraman. K.A.: Toward improving the reliability of hydrologic prediction: model structure uncertainty and its qualification using ensemble-based genetic programming framework. Water Resour. Res. 44(12) (2008)Google Scholar
  4. 4.
    Jyothiprakash, V., Magar, R.B.: Multi-step ahead daily and hourly intermittent reservoir inflow prediction using artificial intelligence techniques using lumped and disturbed data. J. Hydrol. 450, 293–307 (2012)CrossRefGoogle Scholar
  5. 5.
    Babovic, V., Keijzer, M.: Rainfall runoff modelling based on genetic programming. Nord. Hydrol. 33(5), 331–346 (2002)CrossRefGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • K. Bhavita
    • 1
  • D. Swathi
    • 1
  • J. Manideep
    • 1
  • D. Sree Sandeep
    • 1
  • Maheswaran Rathinasamy
    • 1
    Email author
  1. 1.Department of Civil EngineeringMVGR College of EngineeringVizianagaramIndia

Personalised recommendations