Abstract
This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy.
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Acknowledgments
The data used in this study were downloaded from the web server of the USGS. The author wishes to thank the staff of the USGS who are associated with data observation, processing, and management of USGS websites.
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Kişi, Ö. Daily suspended sediment estimation using neuro-wavelet models. Int J Earth Sci (Geol Rundsch) 99, 1471–1482 (2010). https://doi.org/10.1007/s00531-009-0460-2
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DOI: https://doi.org/10.1007/s00531-009-0460-2