Comparative Study of ANNs versus Parametric Methods in Rainfall Frequency Analysis
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VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 14, Issue 2
Abstract
Quantile estimation in rainfall/flood frequency analysis is very important in engineering design of water infrastructure. Many existing methods are based on parametric modeling with the assumption that the underlying probability distribution is known a priori. The estimation performance hence relies largely on the assumed distribution of the observations in addition to the historical measurements. If the distribution is not appropriate to describe the observations, the estimated parameters are prone to large errors. In this paper, artificial neural network and fuzzy logic based methods are used to obtain quantile estimates which avoid the difficult problem of distribution determination while increasing the accuracy of the estimated quantiles. A complete comparison with the conventional parametric methods is given through realistic annual maximum daily rainfall data and Monte Carlo simulations for various sample sizes. The results demonstrate that the artificial neural network techniques yield higher accuracy in quantile estimation than the conventional parametric methods for all sample sizes, particularly in the upper tail region of the frequency curve.
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Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 2 • February 2009
Pages: 172 - 184
Copyright
© 2009 ASCE.
History
Received: Jun 20, 2007
Accepted: May 21, 2008
Published online: Feb 1, 2009
Published in print: Feb 2009
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