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

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• 作者：Ozgur Kisi ; Coskun Ozkan
• 关键词：Suspended sediment modeling ; Local weighted linear regression ; Least square support vector machine ; Neural networks ; Rating curve
• 刊名：Water Resources Management
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：31
• 期：1
• 页码：1-23
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Hydrogeology; Hydrology/Water Resources; Geotechnical Engineering & Applied Earth Sciences; Atmospheric Sciences; Civil Engineering; Environment, general;
• 出版者：Springer Netherlands
• ISSN：1573-1650
• 卷排序：31

文摘

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.