Prediction of water quality in Riva River watershed

• Autorzy: Oz Nurtac , Topal Bayram , Uzun Halil Ibrahim

Identyfikatory

DOI

10.1515/eces-2019-0051

Warianty tytułu

PL

Prognozowanie jakości wody w zlewni rzeki Riva

• Języki publikacji: EN

Abstrakty

EN

The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH₄-N explained BOD and BOD, NH₄-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH₄-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH₄-N.

Słowa kluczowe

EN

Riva river; water quality; artificial neural network; ANN; decision tree; regression models

PL

rzeka Riva; jakość wody; sztuczna sieć neuronowa; ANN; drzewo decyzyjne; modele regresji

• Wydawca: Towarzystwo Chemii i Inżynierii Ekologicznej
• Czasopismo: Ecological Chemistry and Engineering. S
• Rocznik: 2019
• Tom: Vol. 26, nr 4
• Strony: 727--742
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Oz Nurtac

• Environmental Engineering Department, Sakarya University, Esentepe Campus 54187 Sakarya, Turkey, phone [phone], fax [phone]

autor

Topal Bayram

• Business Administration Department, Sakarya University, Esentepe Campus 54187 Sakarya, Turkey

autor

Uzun Halil Ibrahim

• Environmental Engineering Department, Yildiz Technical University, Davutpasa Campus, 34220 Istanbul, Turkey

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).