Advertisement

Water Resources Management

, Volume 13, Issue 3, pp 219–231 | Cite as

A Genetic Programming Approach to Rainfall-Runoff Modelling

  • Dragan A. Savic
  • Godfrey A. Walters
  • James W. Davidson
Article

Abstract

Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a ‘transparent’ and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models.

artificial neural networks genetic programming identification rainfall-runoff modelling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Babovic, V.: 1996, Emergence, Evolution, Intelligence: Hydroinformatics, Balkema, Rotterdam.Google Scholar
  2. Babovic, V. and Abbott, M. B.: 1997, The evolution of equations from hydraulic data, Part II: Applications, J. Hydraulic Res. 35, 411–430.Google Scholar
  3. Cousin, N. and Savic, D. A.: 1997, A rainfall-runoff model using genetic programming, Centre For Systems And Control Engineering, Report No. 97/03, School of Engineering, University of Exeter, Exeter, United Kingdom, p. 70.Google Scholar
  4. Eeles, C. W. O., Parks, Y. and Barr, A.: 1989, HYRROM Operation Manual, Institute of Hydrology, Wallingford, Oxfordshire, U.K.Google Scholar
  5. Eeles, C. W. O. and Blackie, J. R.: 1993, Land use changes in the Balquhidder catchments simulated by a daily streamflow model, J. Hydrology 145, 315–336.Google Scholar
  6. Eeles, CWO: 1994, Parameter optimization of conceptual hydrological models, PhD Thesis, Open University, Milton Keynes, U.K.Google Scholar
  7. Eeles, C. W. O.: 1996, Personal communication.Google Scholar
  8. Goldberg, D. E.: 1989, Genetic Algorithms in Search, Optimisation and Machine Learning, Addison Wesley, Reading, Mass., U.S.A.Google Scholar
  9. Holland, J. H.: 1975, Adaptation in Natural and Artificial Systems, Ann Arbor Science Press, Ann Arbor, U.S.A.Google Scholar
  10. Jacq, F. and Savic, D. A.: 1997, Rainfall-Runoff Modelling Using Neural Networks, Centre For Systems And Control Engineering, Report No. 97/02, School of Engineering, University of Exeter, Exeter, United Kingdom, p. 66.Google Scholar
  11. Karunanithi, N., Greeney, W. J., Darrell Whitley and Bovee, K.: 1994, Neural network for river flow prediction, J. Comp. Civil Engineering ASCE 8, 201–220.Google Scholar
  12. Minns, A.W. and Hall, M. J.: 1996, Artificial neural networks as rainfall-runoff models, Hydrological Sci. J. 41, 399–417.Google Scholar
  13. Nash, J. E.: 1958, The form of the instantaneous unit hydrograph, General Assembly of Toronto, Inter. Assoc. Sci. Hydrol. (Gentbrugge) Pub. 42, compt. Rend. 3, pp. 114–118.Google Scholar
  14. Pöyhönen, H. O. and Savic, D. A.: 1996, Symbolic regression using object-orineted genetic programming (in C++), Centre For Systems And Control Engineering, Report No. 96/04, School of Engineering, University of Exeter, Exeter, United Kingdom, p. 72.Google Scholar
  15. Smith, J. and Eli, R. N.: 1995, Neural-network models of rainfall-runoff process, J. Water Res. Plann. Manage. ASCE 121, 499–508.Google Scholar
  16. Todini, E.: 1988, Rainfall-runoff modelling-past, present and future, J. Hydrology 100, 341–352.Google Scholar
  17. Zhu, M. L. and Fujita, M.: 1994, Comparisons between fuzzy reasoning and neural network methods to forecast runoff discharge, J. Hydroscience Hydraulic Engng. 12, 131–141.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Dragan A. Savic
    • 1
  • Godfrey A. Walters
    • 1
  • James W. Davidson
    • 1
  1. 1.The Centre for Water Systems, School of Engineering and Computer Science, Department of EngineeringUniversity of ExeterExeterUnited Kingdom

Personalised recommendations