Determination of the Effects of Some Water Quality Parameters on Turbidity Parameters in Filyos River with Artificial Neural Network

Berna Aksoy; İsmail Hakkı Özölçer; Emrah Doğan

Makale detayları

Determination of the Effects of Some Water Quality Parameters on Turbidity Parameters in Filyos River with Artificial Neural Network

Yazarlar:

Berna Aksoy | İsmail Hakkı Özölçer | Emrah Doğan

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Su Kaynakları Mühendisliği | İnşaat Mühendisliği (Diğer)

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 2, pp.85-91

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323648

pISSN:

2146-7277

Anahtar kelimeler:

Özet:

 During the water cycle, substances that are contaminated in water cause physical, chemical or biological alterations of the waters natural features, therefore environmental balance deteriorate over time. Observations and measurements on a river give the necessary information about how to benefit from the river. For this reason, it is important to investigate the water quality in rivers and water reservoirs which are close to settlement areas. In this study, surface water quality measurements were carried out at five observation stations along the main line of the Filyos River, which forms the largest sub-basin in the Western Black Sea Basin, at intervals of thirty days in 2015 year. The turbidity parameter was estimated by artificial neural networks ANNs based on water quality parameters such as chromium Cr3, chemical oxygen demand COD, iron Fe3, aluminium Al3, suspended solids, manganese Mn2, zinc Zn2, lead Pb2 and calcium Ca2. The study was conducted with creating two scenarios. In the first scenario, the determined parameters were analyzed by ANN for each station one by one. The obtained data showed that Cr coefficient of determination R2 0.9999 parameter gave the best performance in the estimation of turbidity parameter in the study area. In the second scenario, eight models were created by adding the other best performing parameters one by one to the best performing Cr parameter. The third model formed by Cr, COD, Fe and Al parameters gave the closest result with R20.9992.

 During the water cycle, substances that are contaminated in water cause physical, chemical or biological alterations of the waters natural features, therefore environmental balance deteriorate over time. Observations and measurements on a river give the necessary information about how to benefit from the river. For this reason, it is important to investigate the water quality in rivers and water reservoirs which are close to settlement areas. In this study, surface water quality measurements were carried out at five observation stations along the main line of the Filyos River, which forms the largest sub-basin in the Western Black Sea Basin, at intervals of thirty days in 2015 year. The turbidity parameter was estimated by artificial neural networks ANNs based on water quality parameters such as chromium Cr3, chemical oxygen demand COD, iron Fe3, aluminium Al3, suspended solids, manganese Mn2, zinc Zn2, lead Pb2 and calcium Ca2. The study was conducted with creating two scenarios. In the first scenario, the determined parameters were analyzed by ANN for each station one by one. The obtained data showed that Cr coefficient of determination R2 0.9999 parameter gave the best performance in the estimation of turbidity parameter in the study area. In the second scenario, eight models were created by adding the other best performing parameters one by one to the best performing Cr parameter. The third model formed by Cr, COD, Fe and Al parameters gave the closest result with R20.9992.

 During the water cycle, substances that are contaminated in water cause physical, chemical or biological alterations of the waters natural features, therefore environmental balance deteriorate over time. Observations and measurements on a river give the necessary information about how to benefit from the river. For this reason, it is important to investigate the water quality in rivers and water reservoirs which are close to settlement areas. In this study, surface water quality measurements were carried out at five observation stations along the main line of the Filyos River, which forms the largest sub-basin in the Western Black Sea Basin, at intervals of thirty days in 2015 year. The turbidity parameter was estimated by artificial neural networks ANNs based on water quality parameters such as chromium Cr3, chemical oxygen demand COD, iron Fe3, aluminium Al3, suspended solids, manganese Mn2, zinc Zn2, lead Pb2 and calcium Ca2. The study was conducted with creating two scenarios. In the first scenario, the determined parameters were analyzed by ANN for each station one by one. The obtained data showed that Cr coefficient of determination R2 0.9999 parameter gave the best performance in the estimation of turbidity parameter in the study area. In the second scenario, eight models were created by adding the other best performing parameters one by one to the best performing Cr parameter. The third model formed by Cr, COD, Fe and Al parameters gave the closest result with R20.9992.

 Su döngüsü sırasında suya bulaşan maddeler, suyun fiziksel, kimyasal veya biyolojik özelliklerini değiştirerek su kirliliğine yol açar ve zamanla çevresel dengenin bozulmasına neden olur. Bir nehir üzerinde yapılan gözlemler ve ölçümler, nehirden nasıl yararlanılacağı konusunda gerekli bilgileri verir. Bu nedenle yerleşim yerlerine yakın olan akarsularda ve su depolarında su kalitesinin araştırılması önemlidir. Bu çalışmada, Batı Karadeniz Havzasının en büyük alt havzasını oluşturan Filyos Nehrinin ana hattı boyunca uzanan beş gözlem istasyonunda bir yıllık periyotta otuz gün aralıklarla yüzeysel su kalitesi ölçümleri yapılmıştır. Su kalitesi parametrelerinden, krom Cr3, kimyasal oksijen ihtiyacı COD, demir Fe3, alüminyum Al3, askıda katı madde, mangan Mn2, çinko Zn2, kurşun Pb2 ve kalsiyum Ca2 parametrelerine dayalı olarak bulanıklık parametresinin yapay sinir ağları YSA ile tahmini yapılmıştır. Çalışma iki senaryo üzerinden yürütülmüştür. Birinci senaryoda belirlenen parametreler, her istasyon için tek tek YSA ile analiz edilmiştir. Elde edilen veriler, çalışma alanında, bulanıklık parametresi tahmininde en iyi performansı Cr R20.9999 parametresinin verdiğini göstermiştir. İkinci senaryo da ise en iyi performansı veren Cr parametresine diğer en iyi performansı veren parametreler tek tek eklenerek sekiz model oluşturulmuştur. Cr, KOİ, Fe ve Al parametrelerinin oluşturduğu üçüncü model R20.9992 gerçeğe en yakın sonucu veren model olmuştur.

Makale detayları

Determination of the Effects of Some Water Quality Parameters on Turbidity Parameters in Filyos River with Artificial Neural Network

Yazarlar:

Berna Aksoy | İsmail Hakkı Özölçer | Emrah Doğan

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Su Kaynakları Mühendisliği | İnşaat Mühendisliği (Diğer)

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 2, pp.85-91

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323648

pISSN:

2146-7277

Artificial neural network

Filyos river

turbidity

water qualityBulanıklık

Filyos nehri

su kalitesi

yapay sinir ağı

Sayı

Makale detayları

Yazarlar: Berna Aksoy | İsmail Hakkı Özölçer | Emrah Doğan

Yayıncı: Zonguldak Bulent Ecevit University

Ana Başlıklar: Su Kaynakları Mühendisliği | İnşaat Mühendisliği (Diğer)

Dergi: Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 2, pp.85-91

Belge Tipi: Makale

Yayın Tarihi: 7/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001323648

pISSN: 2146-7277

Sayı

Yazarlar:

Berna Aksoy | İsmail Hakkı Özölçer | Emrah Doğan

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Su Kaynakları Mühendisliği | İnşaat Mühendisliği (Diğer)

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 2, pp.85-91

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323648

pISSN:

2146-7277