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Title: Prediction of suspended sediment concentration from water quality variables
Authors: Bayram, Adem
Kankal, Murat
Tayfur, Gökmen
Önsoy, Hızır
Keywords: Artificial neural networks
Regression analysis
Stream Harsit
Suspended sediment concentration
Total chromium
Total iron
Issue Date: Apr-2014
Publisher: Springer Verlag
Source: Bayram, A., Kankal, M., Tayfur, G., and Önsoy, H. (2014). Prediction of suspended sediment concentration from water quality variables. Neural Computing and Applications, 24(5), 1079-1087. doi:10.1007/s00521-012-1333-3
Abstract: This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.
ISSN: 0941-0643
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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