Prediction of suspended sediment concentration from water quality variables

BAYRAM A., Kankal M., Tayfur G., Onsoy H.

NEURAL COMPUTING & APPLICATIONS, cilt.24, sa.5, ss.1079-1087, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 5
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s00521-012-1333-3
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1079-1087
  • Anahtar Kelimeler: Artificial neural networks, Regression analysis, Stream Harsit, Suspended sediment concentration, Total chromium, Total iron, Turbidity, ARTIFICIAL NEURAL-NETWORKS, TURBIDITY MEASUREMENTS, LOAD CONCENTRATION, OXYGEN-DEMAND, TURKEY, RIVERS, FUZZY
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

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.