Water Resources Management

, Volume 31, Issue 12, pp 3975–3992 | Cite as

A Genetic Programming Approach to System Identification of Rainfall-Runoff Models

  • Jayashree ChadalawadaEmail author
  • Vojtech Havlicek
  • Vladan Babovic


Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for future uncertainty. As a preliminary step, GP is used in conjunction with a conceptual rainfall-runoff model to solve model configuration problem. Two datasets belonging to a tropical catchment of Singapore and a temperate catchment of South Island, New Zealand with contrasting characteristics are analyzed in this study. The results indicate that proposed approach successfully combines the merits of evolutionary algorithm and conceptual knowledge in the generation of optimal model structure and associated parameters to capture runoff dynamics of catchments.


Automatic model generation Conceptual modelling Genetic programming Rainfall-Runoff models System identification 



The authors would like to thank Dr. Ali Meshgi ( for Kent Ridge catchment dataset and Dr. Fabrizio Fenicia ( for Maimai catchment dataset. They are also grateful to reviewers for their insightful comments for improving the manuscript.


  1. Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142(1):47–69CrossRefGoogle Scholar
  2. Babovic V (1996) Emergence, evolution intelligence: hydroinformatics. TU Delft, Delft University of TechnologyGoogle Scholar
  3. Babovic V (2000) Data mining and knowledge discovery in sediment transport. Comput-Aided Civil Infrast Eng 15(5):383–389CrossRefGoogle Scholar
  4. Babovic V, Keijzer M (2000) Genetic programming as a model induction engine. J Hydroinf 2(1):35–60Google Scholar
  5. Basri H (2013) Development of rainfall-runoff model using tank model: Problems and challenges in Province of Aceh, Indonesia. Aceh Int J Sci Technol 2:1Google Scholar
  6. Bautu A, Bautu E (2006) Meteorological data analysis and prediction by means of genetic programming. In: Proceedings of the 5th workshop on mathematical modeling of environmental and life sciences problems constanta. Romania, pp 35–42Google Scholar
  7. Charizopoulos N, Psilovikos A (2016) Hydrologic processes simulation using the conceptual model Zygos: the example of Xynias drained Lake catchment (central Greece). Environ Earth Sci 75(9):1–15CrossRefGoogle Scholar
  8. Deng Y, Cardin MA, Babovic V, Santhanakrishnan D, Schmitter P, Meshgi A (2013) Valuing flexibilities in the design of urban water management systems. Water Res 47(20):7162–7174CrossRefGoogle Scholar
  9. Dorado J, Rabuñ AL JR, Pazos A, Rivero D, Santos A, Puertas J (2003) Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Appl Artif Intell 17(4):329–343CrossRefGoogle Scholar
  10. Euser T, Winsemius H, Hrachowitz M, Fenicia F, Uhlenbrook S, Savenije H (2013) A framework to assess the realism of model structures using hydrological signatures. Hydrol Earth Syst Sci 17(5):1893–1912CrossRefGoogle Scholar
  11. Fallah-Mehdipour E, Haddad OB, Marino MA (2014) Genetic programming in groundwater modeling. J Hydrol Eng 19(12):04014,031CrossRefGoogle Scholar
  12. Fenicia F, Kavetski D, Savenije HH (2011) Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development. Water Resour Res 47:11CrossRefGoogle Scholar
  13. Franchini M, Pacciani M (1991) Comparative analysis of several conceptual rainfall-runoff models. J Hydrol 122(1-4):161–219CrossRefGoogle Scholar
  14. Füssel HM (2007) Vulnerability: a generally applicable conceptual framework for climate change research. Global Environ Change 17(2):155–167CrossRefGoogle Scholar
  15. Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377(1):80–91CrossRefGoogle Scholar
  16. Havlicek V, Hanel M, Máca P, Kuraz M, Pech P (2013) Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting. Computing 95(1):363–380CrossRefGoogle Scholar
  17. Hermanovsky M, Havlicek V, Hanel M, Pech P (2017) Regionalization of runoff models derived by genetic programming. J Hydrol 547:544–556CrossRefGoogle Scholar
  18. Keijzer M, Foster J (2007) Crossover bias in genetic programming. In: European conference on genetic programming. Springer, pp 33–44Google Scholar
  19. Khu ST, Liong SY, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forecasting1. JAWRA J Amer Water Resour Assoc 37(2):439–451CrossRefGoogle Scholar
  20. Kommenda M, Beham A, Affenzeller M, Kronberger G (2015) Complexity measures for multi-objective symbolic regression. In: International conference on computer aided systems theory. Springer, pp 409–416Google Scholar
  21. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT pressGoogle Scholar
  22. Liong SY, Gautam TR, Khu ST, Babovic V, Keijzer M, Muttil N (2002) Genetic programming: a new paradigm in rainfall runoff modeling. JAWRA J Amer Water Resour Assoc 38(3):705–718CrossRefGoogle Scholar
  23. Londhe S, Charhate S (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol Sci J–J des Sciences Hydrologiques 55(7):1163–1174CrossRefGoogle Scholar
  24. Madsen H (2000) Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J Hydrol 235(3):276–288CrossRefGoogle Scholar
  25. McGlynn BL, McDonnel JJ, Brammer DD (2002) A review of the evolving perceptual model of hillslope flowpaths at the Maimai catchments, New Zealand. J Hydrol 257(1):1–26CrossRefGoogle Scholar
  26. Meshgi A, Schmitter P, Chui TFM, Babovic V (2015) Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using genetic programming. J Hydrol 525:711–723CrossRefGoogle Scholar
  27. Monteith J (1965) The state and movement of water in living organisms. In: Proc. evaporation and environment, XIXth Symp, pp 205–234Google Scholar
  28. Muttil N, Lee JH (2005) Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecol Modell 189(3):363–376CrossRefGoogle Scholar
  29. Oyebode OK, Adeyemo JA (2014) Genetic programming: principles, applications and opportunities for hydrological modelling. World Acad Sci Eng Technol Int J Environ Chem Ecol Geol Geophys Eng 8(6):348–354Google Scholar
  30. Pinkus AZ, Winitzki S (2002) Yacas: a do-it-yourself symbolic algebra environment. In: Artificial intelligence, automated reasoning, and symbolic computation. Springer, pp 332–336Google Scholar
  31. Refsgaard JC, Abbott M (1996) Distributed hydrological modelling. Kluwer AcademicGoogle Scholar
  32. Rowe L, Pearce A, O’Loughlin C (1994) Hydrology and related changes after harvesting native forest catchments and establishing Pinus radiata plantations. Part 1. Introduction to study. Hydrol Process 8(3):263–279CrossRefGoogle Scholar
  33. Selle B, Muttil N (2011) Testing the structure of a hydrological model using Genetic Programming. J Hydrol 397(1):1–9CrossRefGoogle Scholar
  34. Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol 3. ICSI BerkeleyGoogle Scholar
  35. Sugawara M (1979) Automatic calibration of the tank model/L’étalonnage automatique d’un modèle à cisterne. Hydrol Sci J 24(3):375–388CrossRefGoogle Scholar
  36. Team R Core (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013Google Scholar
  37. Vanneschi L, Castelli M, Silva S (2010) Measuring bloat, overfitting and functional complexity in genetic programming. In: Proceedings of the 12th annual conference on genetic and evolutionary computation. ACM, pp 877–884Google Scholar
  38. Wang W, Xu D, Qiu L, Ma J (2009) Genetic programming for modelling long-term hydrological time series. In: 2009 Fifth international conference on natural computation, vol 4. IEEE, pp 265–269Google Scholar
  39. Whigham P, Crapper P (2001) Modelling rainfall-runoff using genetic programming. Math Comput Model 33(6):707–721CrossRefGoogle Scholar
  40. Winkler S, Affenzeller M, Wagner S, Kronberger G, Kommenda M (2012) Using genetic programming in nonlinear model identification. In: Identification for automotive systems. Springer, pp 89–109Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Jayashree Chadalawada
    • 1
    Email author
  • Vojtech Havlicek
    • 2
  • Vladan Babovic
    • 1
  1. 1.Department of Civil and Environmental EngineeringNational University of SingaporeSingaporeSingapore
  2. 2.Faculty of Environmental SciencesCzech University of Life Sciences PraguePragueCzech Republic

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