Abstract
Forecasting of sediment concentration in rivers is a very important process for water resources assignment development and management. In this paper, a neural network approach is proposed to predict suspended sediment concentration from streamflow. A comparison was performed between artificial neural network, sediment rating-curve and multilinear regression models. It was based on a 5 years period of continuous streamflow, suspended sediment concentration and mean water temperature data of West Virginia, Little Coal River, Danville station operated by the United States Geological Survey. Based on comparison of the results, it is found that the artificial neural network model gives better estimates than the sediment rating-curve and multilinear regression techniques.
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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 Web sites.
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Demirci, M., Üneş, F., Saydemir, S. (2015). Suspended Sediment Estimation Using an Artificial Intelligence Approach. In: Heininger, P., Cullmann, J. (eds) Sediment Matters. Springer, Cham. https://doi.org/10.1007/978-3-319-14696-6_6
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DOI: https://doi.org/10.1007/978-3-319-14696-6_6
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