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Prediction of Water Quality in Riva River Watershed
- Source :
- Ecological Chemistry and Engineering S; December 2019, Vol. 26 Issue: 4 p727-742, 16p
- Publication Year :
- 2019
-
Abstract
- 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-Tand COD-Twere correlated and the regression analysis showed that COD, TKNand NH4-N explained BOD and BOD, NH4-N, Tand TSSexplained 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 ANNwas the best prediction model for COD, the Decision Tree for BOD and the ANNand Decision Tree for NH4-N.
Details
- Language :
- English
- ISSN :
- 20844549 and 18986196
- Volume :
- 26
- Issue :
- 4
- Database :
- Supplemental Index
- Journal :
- Ecological Chemistry and Engineering S
- Publication Type :
- Periodical
- Accession number :
- ejs52167373
- Full Text :
- https://doi.org/10.1515/eces-2019-0051