Açık Akademik Arşiv Sistemi

PREDICTION OF WATER QUALITY IN RIVA RIVER WATERSHED

Show simple item record

dc.contributor.authors Oz, N; Topal, B; Uzun, HI;
dc.date.accessioned 2020-02-25T07:59:24Z
dc.date.available 2020-02-25T07:59:24Z
dc.date.issued 2019
dc.identifier.citation Oz, N; Topal, B; Uzun, HI; (2019). PREDICTION OF WATER QUALITY IN RIVA RIVER WATERSHED. ECOLOGICAL CHEMISTRY AND ENGINEERING S-CHEMIA I INZYNIERIA EKOLOGICZNA S, 26, 742-727
dc.identifier.issn 1898-6196
dc.identifier.uri https://doi.org/10.1515/eces-2019-0051
dc.identifier.uri https://hdl.handle.net/20.500.12619/45559
dc.description.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-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.
dc.language English
dc.publisher SOC ECOLOGICAL CHEMISTRY & ENGINEERING
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject Environmental Sciences & Ecology
dc.title PREDICTION OF WATER QUALITY IN RIVA RIVER WATERSHED
dc.type Article
dc.identifier.volume 26
dc.identifier.startpage 727
dc.identifier.endpage 742
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü
dc.contributor.saüauthor Öz, Nurtaç
dc.contributor.saüauthor Topal, Bayram
dc.relation.journal ECOLOGICAL CHEMISTRY AND ENGINEERING S-CHEMIA I INZYNIERIA EKOLOGICZNA S
dc.identifier.wos WOS:000505159900009
dc.identifier.doi 10.1515/eces-2019-0051
dc.contributor.author Öz, Nurtaç
dc.contributor.author Topal, Bayram
dc.contributor.author Halil Ibrahim Uzun


Files in this item

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/openAccess Except where otherwise noted, this item's license is described as info:eu-repo/semantics/openAccess