Comparative Studies in Problems of Missing Extreme Daily Streamflow Records
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 1
Abstract
This study evaluates the performance of different estimation techniques for the infilling of missing observations in extreme daily hydrologic series. Generalized regression neural networks (GRNNs) are proposed for the estimation of missing observations with their input configuration determined through an optimization approach of genetic algorithm (GA). The efficacy of the GRNN-GA technique was obtained through comparative performance analyses of the proposed technique to existing techniques. Based on the results of such comparative analyses, especially in the case of the English River (Canada), the GRNN-GA technique was found to be a highly competitive method when compared to the existing artificial neural networks techniques. In addition, based on the criteria of mean squared and absolute errors, a detailed comparative analysis involving the GRNN-GA, -nearest neighbors, and multiple imputation for the infilling of missing records of the Saugeen River (Canada), also found the GRNN-GA technique to be superior when evaluated against other competing techniques.
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Acknowledgments
The second writer wishes to acknowledge the financial support given to this research by the Natural Sciences and Engineering Research Council (NSERC) of Canada through Grant No. NSERCOGP-0004404.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 1 • January 2009
Pages: 91 - 100
Copyright
© 2009 ASCE.
History
Received: Apr 10, 2006
Accepted: Jul 21, 2008
Published online: Jan 1, 2009
Published in print: Jan 2009
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