Modified K-NN Model for Stochastic Streamflow Simulation
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 4
Abstract
This paper presents a lag-1 modified K-nearest neighbor (K-NN) approach for stochastic streamflow simulation. The simulation at any time given the value at the time involves two steps: (1) obtaining the conditional mean from a local polynomial fitted to the historical values of time and , and (2) then resampling (i.e., bootstrapping) a residual at one of the historical observations and adding it to the conditional mean. The residuals are resampled using a probability metric that gives more weight to the nearest neighbor and less to the farthest. The “residual resampling” step is the modification to the traditional K-NN time-series bootstrap approach, which enables the generation of values not seen in the historical record. This model is applied to monthly streamflow at the Lees Ferry stream gauge on the Colorado River and is compared to both a parametric periodic autoregressive and a nonparametric index sequential method for streamflow generation, each widely used in practice. The modified K-NN approach is found to exhibit better performance in terms of capturing the features present in the data.
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Acknowledgments
Funding for this work by the Bureau of Reclamation is gratefully acknowledged. The writers thank Dave Trueman for his strong support and encouragement. The writers would also like to thank three anonymous reviewers whose comments significantly improved the paper.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 11 • Issue 4 • July 2006
Pages: 371 - 378
Copyright
© 2006 ASCE.
History
Received: Jan 9, 2004
Accepted: Sep 14, 2005
Published online: Jul 1, 2006
Published in print: Jul 2006
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