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Water Resources Management

, Volume 31, Issue 1, pp 1–23 | Cite as

A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression

  • Ozgur Kisi
  • Coskun Ozkan
Article
  • 433 Downloads

Abstract

Accurate estimation of suspended sediment is important for water resources projects. The accuracy of local weighted linear regression (LWLR) technique is investigated in this study for modeling streamflow-suspended sediment relationship. Daily data from two stations on the Eel River in California were used in the applications. In the first part of the study, the LWLR results were compared with those of the least square support vector machine (LSSVM), artificial neural networks (ANNs) and sediment rating curve (SRC) for modeling sediment data of upstream and downstream stations, separately. Root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R2) statistics were used for comparison of the applied models Comparison results indicated that the LWLR model performed better than the LSSVM, ANN and SRC models. Accuracies of the sediment modeling increased by the LWLR model compared with the LSSVM model: 14 % (60 %) and 33 % (42 %) decrease in the RMSE (MAE) values for the upstream and downstream stations, respectively. The second part of the study focused on the comparison of the models in estimating downstream suspended sediment data by using data from both stations. LWLR was found to be better than the LSSVM, ANN and SRC models. The RMSE accuracy of the LSSVM model was increased by 39 % using the LWLR model.

Keywords

Suspended sediment modeling Local weighted linear regression Least square support vector machine Neural networks Rating curve 

Notes

Acknowledgements

The data used in this study were downloaded from the web server of the USGS. The author would like to thank the personnels of the USGS who are associated with data observation, processing, and management of USGS Web sites.

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Center for Interdisciplinary ResearchInternational Black Sea UniversityTbilisiGeorgia
  2. 2.Engineering Faculty, Geomatics Engineering DepartmentErciyes UniversityKayseriTurkey

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