Abstract
The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been spent to model this process, and many hydrologic models have been built specifically for this purpose. These models are generally known as a rainfall — runoff (R-R) models.
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References
Babovic, V., Keijzer M., Genetic programming as a model induction engine, 2000, Journal of Hydroinformatics 02.1, pp.35–60.
Flerchinger, G. N., Cooley, K. R., 2000, A ten-year water balance of a mountainous semi-arid watershed, Journal of hydrology 237, pp. 86–90.
Kuffler, S. W., Nichols, J. G., Martin, A. R., 1984, From Neuron to Brain, second edition, Sinauer.
Minns, A. W., 1998, Artificial Neural Networks as Subsymbolic Process Descriptors, PhD thesis, Balkema, Rotterdam.
Shawkat Ali, Md., 1997, Application of Self Organizing Feature Maps for the Analysis of Hydrological and Ecological Data Sets, MSc Thesis HH 315, IHE-Delft
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© 2003 Springer Science+Business Media Dordrecht
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Bojkov, V. (2003). An Approach for Runoff Computation Using Three Data Mining Techniques. In: Arsov, R., Marsalek, J., Watt, E., Zeman, E. (eds) Urban Water Management: Science Technology and Service Delivery. NATO Science Series, vol 25. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0057-4_14
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DOI: https://doi.org/10.1007/978-94-010-0057-4_14
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-1540-3
Online ISBN: 978-94-010-0057-4
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