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

Author

Listed:
  • Ozgur Kisi

    (International Black Sea University)

  • Coskun Ozkan

    (Erciyes University)

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.

Suggested Citation

  • Ozgur Kisi & Coskun Ozkan, 2017. "A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 1-23, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1481-9
    DOI: 10.1007/s11269-016-1481-9
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    References listed on IDEAS

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    1. Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Vlassios Hrissanthou, 2014. "Machine Learning Utilization for Bed Load Transport in Gravel-Bed Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3727-3743, September.
    2. Aman Mohammad Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
    3. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521424318, October.
    4. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    5. Kisi, Özgür, 2008. "Constructing neural network sediment estimation models using a data-driven algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(1), pages 94-103.
    6. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "Erratum to: River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 2123-2123, May.
    7. Thorsnes, Paul & McMillen, Daniel P, 1998. "Land Value and Parcel Size: A Semiparametric Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 17(3), pages 233-244, November.
    8. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521370905, October.
    9. Aman Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
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