Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology
A. Elshorbagy et al.
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Subject: Engineering Hydrology | Techniques and Approaches: Mathematical applications
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Cited articles
Abrahart, R., See, L., and Solomatine, D., (Eds): Practical hydroinformatics. Computational intelligence and technological developments in water applications, Springer-Verlag, Berlin, Heidelberg, Germany, 505 pp., 2008.
Abrahart, R., See, L., and Dawson, C.: Neural Network hydroinformatics: Maintaining scientific rigour, in: Practical hydroinformatics. Computational intelligence and technological developments in water applications, edited by: Abrahart, R., See, L., and Solomatine, D., Springer-Verlag, Berlin, Heidelberg, Germany, 33–47, 2008.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial neural networks in hydrology, I: Preliminary concepts, J. Hydrol. Eng., 5(Eq. (2)), 115–123, 2000.
Babovic, V. and Keijzer, M.: Rainfall-runoff modelling based on genetic programming, Nord. Hydrol., 33(5), 331–346, 2002.
Babovic, V. and Keijzer, M.: Genetic Programming as Model Induction Engine, J. Hydroinform., 2(Eq. (1)), 35–60, 2000.
