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

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Abstract

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.

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References

  • Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142(1):47–69

    Article  Google Scholar 

  • Babovic V (1996) Emergence, evolution intelligence: hydroinformatics. TU Delft, Delft University of Technology

  • Babovic V (2000) Data mining and knowledge discovery in sediment transport. Comput-Aided Civil Infrast Eng 15(5):383–389

    Article  Google Scholar 

  • Babovic V, Keijzer M (2000) Genetic programming as a model induction engine. J Hydroinf 2(1):35–60

    Google Scholar 

  • Basri H (2013) Development of rainfall-runoff model using tank model: Problems and challenges in Province of Aceh, Indonesia. Aceh Int J Sci Technol 2:1

    Google Scholar 

  • 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 35–42

  • 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):1–15

    Article  Google Scholar 

  • 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):7162–7174

    Article  Google Scholar 

  • 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):329–343

    Article  Google Scholar 

  • 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):1893–1912

    Article  Google Scholar 

  • Fallah-Mehdipour E, Haddad OB, Marino MA (2014) Genetic programming in groundwater modeling. J Hydrol Eng 19(12):04014,031

    Article  Google Scholar 

  • Fenicia F, Kavetski D, Savenije HH (2011) Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development. Water Resour Res 47:11

    Article  Google Scholar 

  • Franchini M, Pacciani M (1991) Comparative analysis of several conceptual rainfall-runoff models. J Hydrol 122(1-4):161–219

    Article  Google Scholar 

  • Füssel HM (2007) Vulnerability: a generally applicable conceptual framework for climate change research. Global Environ Change 17(2):155–167

    Article  Google Scholar 

  • 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):80–91

    Article  Google Scholar 

  • 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):363–380

    Article  Google Scholar 

  • Hermanovsky M, Havlicek V, Hanel M, Pech P (2017) Regionalization of runoff models derived by genetic programming. J Hydrol 547:544–556

    Article  Google Scholar 

  • Keijzer M, Foster J (2007) Crossover bias in genetic programming. In: European conference on genetic programming. Springer, pp 33–44

  • 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):439–451

    Article  Google Scholar 

  • 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 409–416

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT press

  • 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):705–718

    Article  Google Scholar 

  • Londhe S, Charhate S (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol Sci J–J des Sciences Hydrologiques 55(7):1163–1174

    Article  Google Scholar 

  • Madsen H (2000) Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J Hydrol 235(3):276–288

    Article  Google Scholar 

  • 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):1–26

    Article  Google Scholar 

  • 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 525:711–723

    Article  Google Scholar 

  • Monteith J (1965) The state and movement of water in living organisms. In: Proc. evaporation and environment, XIXth Symp, pp 205–234

  • Muttil N, Lee JH (2005) Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecol Modell 189(3):363–376

    Article  Google Scholar 

  • 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):348–354

    Google Scholar 

  • Pinkus AZ, Winitzki S (2002) Yacas: a do-it-yourself symbolic algebra environment. In: Artificial intelligence, automated reasoning, and symbolic computation. Springer, pp 332–336

  • Refsgaard JC, Abbott M (1996) Distributed hydrological modelling. Kluwer Academic

  • 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):263–279

    Article  Google Scholar 

  • Selle B, Muttil N (2011) Testing the structure of a hydrological model using Genetic Programming. J Hydrol 397(1):1–9

    Article  Google Scholar 

  • Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol 3. ICSI Berkeley

  • Sugawara M (1979) Automatic calibration of the tank model/L’étalonnage automatique d’un modèle à cisterne. Hydrol Sci J 24(3):375–388

    Article  Google Scholar 

  • Team R Core (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013

  • 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 877–884

  • 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 265–269

  • Whigham P, Crapper P (2001) Modelling rainfall-runoff using genetic programming. Math Comput Model 33(6):707–721

    Article  Google Scholar 

  • 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 89–109

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Acknowledgements

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

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Correspondence to Jayashree Chadalawada.

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Chadalawada, J., Havlicek, V. & Babovic, V. A Genetic Programming Approach to System Identification of Rainfall-Runoff Models. Water Resour Manage 31, 3975–3992 (2017). https://doi.org/10.1007/s11269-017-1719-1

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  • DOI: https://doi.org/10.1007/s11269-017-1719-1

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