Advertisement

Computing

, Volume 95, Supplement 1, pp 363–380 | Cite as

Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting

  • Vojtěch HavlíčekEmail author
  • Martin Hanel
  • Petr Máca
  • Michal Kuráž
  • Pavel Pech
Article

Abstract

This paper focuses on improving rainfall-runoff forecasts by a combination of genetic programming (GP) and basic hydrological modelling concepts. GP is a general optimisation technique for making an automated search of a computer program that solves some particular problem. The SORD! program was developed for the purposes of this study (in the R programming language). It is an implementation of canonical GP. Special functions are used for a combined approach of hydrological concepts and GP. The special functions are a reservoir model, a simple moving average model, and a cumulative sum and delay operator. The efficiency of the approach presented here is tested on runoff predictions for five catchments of various sizes. The input data consists of daily rainfall and runoff series. The forecast step is one day. The performance of the proposed approach is compared with the results of the artificial neural network model (ANN) and with the GP model without special functions. GP combined with these concepts provides satisfactory performance, and the simulations seem to be more accurate than the results of ANN and GP without these functions. An additional advantage of the proposed approach is that it is not necessary to determine the input lag, and there is better convergence. The SORD! program provides an easy-to-use alternative for data-oriented modelling combined with simple concepts used in hydrological modelling.

Keywords

Genetic programming Rainfall-runoff modelling Hydrology Evolutionary algorithms Runoff forecast 

Mathematics Subject Classification (2000)

86A05 68T20 68T05 

Notes

Acknowledgments

Financial support from the Technology Agency of the Czech Republic (research project TA02021249) is gratefully acknowledged. The authors wish to acknowledge the MOPEX project staff, which are associated with data providing and management.

References

  1. 1.
    Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (with contributions by Koza JR)
  2. 2.
    Koza JR (1992) Genetic Programming: on the programming of computers by means of natural selection. MIT Press, USAzbMATHGoogle Scholar
  3. 3.
    Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin, p 320zbMATHCrossRefGoogle Scholar
  4. 4.
    Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, HeidelbergzbMATHCrossRefGoogle Scholar
  5. 5.
    Bäck T, Fogel D, Michalewicz Z (2000) Evolutionary computation 1: basic algorithms and operators. Institute of Physics Publishing, BristolzbMATHCrossRefGoogle Scholar
  6. 6.
    Babovic V (1996) Emergence, evolution, intelligence: hydroinformatics. PhD thesis, International Institute for Infrastructural, Hydraulic and Environmental Engineering and Technical University Delft, The Netherlands (Published by A. A. Balkema Publishers)Google Scholar
  7. 7.
    Babovic V, Abbott MB (1997) The evolution of equations from hydraulic data part i: theory. J Hydraul Res 35(3):397–430CrossRefGoogle Scholar
  8. 8.
    Babovic V, Abbott MB (1997) Evolution of equations from hydraulic data. part ii: applications. J Hydraul Res 35(3):411–430CrossRefGoogle Scholar
  9. 9.
    Cousin N, Savic DA (1997) A rainfall-runoff model using genetic programming. Technical report (report no. 97/03), School of Engineering, University of ExeterGoogle Scholar
  10. 10.
    Drecourt JP (1999) Application of neural networks and genetic programming to rainfall runoff modelling. Techical report, Danish Hydraulic Institute (D2K–0699-1)Google Scholar
  11. 11.
    Savic DA, Walters GA, Davidson JW (1999) A genetic programming approach to rainfall-runoff modelling. Water Resour Manag 13(3):219–231CrossRefGoogle Scholar
  12. 12.
    Babovic V, Keijzer M (2002) Rainfall-runoff modelling based on genetic programming. Nordic Hydrol 33(5):331–346Google Scholar
  13. 13.
    Liong SY, Gautam RR, Khu ST, Babovic V, Keijzer M, Muttil N (2002) Genetic programming: a new paradigm in rainfall runoff modeling. J Am Water Resour Assoc 38(3):705–718CrossRefGoogle Scholar
  14. 14.
    Jayawardena A, Muttil N, Fernando T (2005) Rainfall-runoff modelling using genetic programming. In: Zerger A, Argent R (eds) Proceedings of the MODSIM 2005 international congress on modelling and simulation: advances and applications for management and decision making. Melbourne, Australia, pp 1841–1847Google Scholar
  15. 15.
    Jayawardena AW, Muttil N, Lee JHW (2006) Comparative analysis of data-driven and gis-based conceptual rainfall-runoff model. J Hydrol Eng 11(1):1–11CrossRefGoogle Scholar
  16. 16.
    Rabuñal JR, Puertas J, Suarez J, Rivero D (2007) Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol Process 21(4):476–485CrossRefGoogle Scholar
  17. 17.
    Makkeasorn A, Chang N, Zhou X (2008) Short-term streamflow forecasting with global climate change implications—a comparative study between genetic programming and neural network models. J Hydrol 352(3–4):336–354CrossRefGoogle Scholar
  18. 18.
    Londhe S, Charhate S, Londhe S, Charhate S (2009) Towards modelling of streamflow using soft tools, vol 331. IAHS-AISH Publications, India, pp 245–253Google Scholar
  19. 19.
    Wang WC, Chau KW, Cheng CT, Qiu L (2009a) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306CrossRefGoogle Scholar
  20. 20.
    Watanabe N, Fukami K, Imamura H, Sonoda K, Yamane S (2009) Flood forecasting technology with radar-derived rainfall data using genetic programming. In: Proceedings of the 2009 international joint conference on Neural Networks, IJCNN’09, IEEE Press, Piscataway, NJ, USA, pp 776–783Google Scholar
  21. 21.
    Ni Q, Wang L, Ye R, Yang F, Sivakumar M (2010) Evolutionary modeling for streamflow forecasting with minimal datasets: a case study in the West Malian river, China. Environ Eng Sci 27(5):377–385CrossRefGoogle Scholar
  22. 22.
    Kashid S, Ghosh S, Maity R (2010) Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection. J Hydrol 395(1–2):23–38CrossRefGoogle Scholar
  23. 23.
    Elshorbagy A, Corzo G, Srinivasulu S, Solomatine D (2010) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—part 1: concepts and methodology. Hydrol Earth Syst Sci 14(10):1931–1941CrossRefGoogle Scholar
  24. 24.
    Elshorbagy A, Corzo G, Srinivasulu S, Solomatine D (2010) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—part 2: application. Hydrol Earth Syst Sci 14(10):1943–1961CrossRefGoogle Scholar
  25. 25.
    Londhe S, Charhate S (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol Sci J 55(7):1163–1174CrossRefGoogle Scholar
  26. 26.
    Maheswaran R, Khosa R (2011) Multi resolution genetic programming approach for stream flow forecasting. Lecture notes in computer science 7076 LNCS (part 1), pp 714–722Google Scholar
  27. 27.
    Rabuñal JR, Puertas J, Rivero D, Fraga I, Cea L, Garrido M (2011) Genetic programming for prediction of water flow and transport of solids in a basin. In: Proceedings of the 4th international conference on interplay between natural and artificial computation: new challenges on bioinspired applications, vol part II, IWINAC’11, Springer, Berlin, Heidelberg, pp 223–232Google Scholar
  28. 28.
    Whigham PA, Crapper PF (2001) Modelling rainfall-runoff using genetic programming. Math Comput Modell 33(6–7):707–721zbMATHCrossRefGoogle Scholar
  29. 29.
    Aytek A, Asce M, Alp M (2008) An application of artificial intelligence for rainfall-runoff modeling. J Earth Syst Sci 117(2):145–155CrossRefGoogle Scholar
  30. 30.
    Guven A (2009) Linear genetic programming for time-series modelling of daily flow rate. J Earth Syst Sci 118(2):137–146CrossRefGoogle Scholar
  31. 31.
    Fernando A, Shamseldin A, Abrahart R (2011) Comparison of two data-driven approaches for daily river flow forecasting. In: Proceedings of the MODSIM2011, 19th international congress on modelling and simulation, pp 1077–1083Google Scholar
  32. 32.
    Seckin N, Guven A (2012) Estimation of peak flood discharges at ungauged sites across Turkey. Water Resour Manag 26:2569–2581CrossRefGoogle Scholar
  33. 33.
    Wang WC, Xu D, Qiu L, Ma J (2009b) Genetic programming for modelling long-term hydrological time series. Int Conf Nat Comput 4:265–269Google Scholar
  34. 34.
    Schaake J, Cong S, Duan Q (2006) US MOPEX data set. IAHS Publ Ser 307:9–28Google Scholar
  35. 35.
    R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org, ISBN: 3-900051-07-0
  36. 36.
    Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part i—a discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  37. 37.
    Luke S, Panait L (2002) Fighting bloat with nonparametric parsimony pressure. In: Proceedings of the 7th international conference on parallel problem solving from nature, PPSN VII, Granada, Spain, 7–11 Sept 2002, Springer, Berlin, pp 411–421Google Scholar
  38. 38.
    Kitanidis P, Bras R (1980) Real-time forecasting wih a conceptual hydrologic model. 2. Applications and results. Water Resour Res 16(6):1034–1044CrossRefGoogle Scholar
  39. 39.
    Vladislavleva E, Smits G, den Hertog D (2010) On the importance of data balancing for symbolic regression. IEEE Trans Evolut Comput 14(2):252–277CrossRefGoogle Scholar
  40. 40.
    Eggermont J, Hemert JIv (2001) Adaptive genetic programming applied to new and existing simple regression problems. In: Proceedings of the 4th European conference on genetic programming, EuroGP’01, Springer, Berlin, pp 23–35Google Scholar
  41. 41.
    Silva S, Almeida J (2003) Dynamic maximum tree depth-a simple technique for avoiding bloat in tree-based GP. In: Cantú-Paz E, Foster J, Deb K, Davis L, Roy R, O’Reilly UM, Beyer HG, Standish R, Kendall G, Wilson S, Harman M, Wegener J, Dasgupta D, Potter M, Schultz A, Dowsland K, Jonoska N, Miller J (eds) Genetic and evolutionary computation—GECCO 2003, lecture notes in computer science, vol 2724, Springer, Berlin/Heidelberg, pp 210–210Google Scholar
  42. 42.
    Havlíček V (2011) SORD!—GP tool for hydrological modelling. http://www.kvhem.cz/vyzkum/software/
  43. 43.
    Tukey JW (1977) Exploratory data analysis. Addison-Wesley, Reading, p 688zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Vojtěch Havlíček
    • 1
    Email author
  • Martin Hanel
    • 1
  • Petr Máca
    • 1
  • Michal Kuráž
    • 1
  • Pavel Pech
    • 1
  1. 1.Department of Water Resources and Environmental Modeling, Faculty of Environmental SciencesCzech University of Life Sciences Prague PragueCzech Republic

Personalised recommendations