The rainfall-runoff transformation is a highly complex dynamic process and the development of fast and robust modelling instruments has always been one of the most important topics for hydrology. Over time, a significant number of hydrological models have been developed with a clear trend towards a process-based approach. The downside of these types of models is the significant amount of data required for building the model and for the calibration process: in practice, the collection of all necessary data for such models proves to be a difficult task. In order to cope with this issue, various data-driven modelling techniques have been introduced for hydrological modelling as an alternative to more traditional approaches, on the basis of their capacity of mapping out complex relationships from observation data. Having the capacity to generate meaningful mathematical structures as results, genetic programming (GP) presents a high potential for rainfall-runoff modelling as a data-driven method. Using ground and radar rainfall observation, the aim of this study is to investigate the GP technique capability for modelling the rainfall-runoff process, taking into consideration a flash-flood event.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Solomatine, D. P. & Ostfeld, A. (2008), Data-driven modelling: Some past experiences and new approaches. Journal of Hydroinformatics. 10(1), 3-22. DOI: 10.2166/hydro.2008.015;
 Hsu, K., Vijai Gupta, H., & Sorooshian, S. (1995), Artificial neural network modeling of the rainfall-runoff process. Water Resources Research. 31(10), 2517-2530. DOI: 10.1029/95WR01955;
 Abrahart, R. J. & See, L. M. (2007), Neural network modelling of non-linear hydrological relationships. Hydrology and Earth System Sciences. 11, 1563-1579. DOI: 10.5194/hess-11-1563-2007;
 Minns, A. W. & Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal, 41(3), 399-417;
 Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Natural Selection. Cambridge, MA, USA: MIT Press;
 Savic, D. A., Walters, G. A. and Davidson G. W. (1999). A genetic programming approach to rainfall-runoff modeling. Water Resources Management 13: 219-231;
 Babovic, V., Keijzer, M. (2002). Rainfall runoff modelling based on genetic programming. Nordic hydrology, vol. 33, pp. 331-346;
 Whigham, P. A., Crapper, P. F. (2001). Modelling Rainfall-Runoff Relationships using Genetic Programming. Mathematical and Computer Modelling: An International Journal, Volume 33, pp 707-721. DOI: 10.1016/S0895-7177(00)00274-0;
 Dinu, C., Drobot, R., Pricop, C., Blidaru, T. V. (2017). Flash-flood modelling with artificial neural networks using radar rainfall estimates. Scientific Journal - Mathematical Modeling in Civil Engineering, Vol. 13-No. 3: 10-20 - 2017, Doi:10.1515/mmce-2017-0008;
 Poli R., Langdon W.B., McPhee N.F. (2008). A Field Guide to Genetic Programming. Lulu Enterprises UK Limited;
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning. Reading, Mass., U.S.A: Addison Wesley Addison-Wesley Publishing Company, Inc.;
Goldberg, D.E., Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms, Volume 1, pp 69-93. DOI: 10.1016/B978-0-08-050684-5.50008-2.