Filyos Nehri’nde Bazı Su Kalitesi Parametrelerinin Bulanıklık Parametresi Üzerine Etkilerinin Yapay Sinir Ağı ile Belirlenmesi

Yıl 2024, Cilt: 14 Sayı: 2, 85 - 91, 23.07.2024

Berna Aksoy , İsmail Hakkı Özölçer , Emrah Doğan

Öz

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 Havzası’nın en büyük alt havzasını oluşturan Filyos Nehri’nin ana hattı boyunca uzanan beş gözlem istasyonunda bir yıllık periyotta otuz gün aralıklarla yüzeysel su kalitesi ölçümleri yapılmıştır. Su kalitesi parametrelerinden, krom (Cr+3), kimyasal oksijen ihtiyacı (COD), demir (Fe+3), alüminyum (Al+3), askıda katı madde, mangan (Mn+2), çinko (Zn+2), kurşun (Pb+2) ve kalsiyum (Ca+2) 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 (R2=0.9999) 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 R2=0.9992 gerçeğe en yakın sonucu veren model olmuştur.

Anahtar Kelimeler

Bulanıklık, Filyos nehri, su kalitesi, yapay sinir ağı

Destekleyen Kurum

Zonguldak Bülent Üniversitesi BAP Birimi

Proje Numarası

2015_37891158_02

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

Öz

During the water cycle, substances that are contaminated in water cause physical, chemical or biological alterations of the water’s 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 (Cr+3), chemical oxygen demand (COD), iron (Fe+3), aluminium (Al+3), suspended solids, manganese (Mn+2), zinc (Zn+2), lead (Pb+2) and calcium (Ca+2). 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 R2=0.9992.

Anahtar Kelimeler

Artificial neural network, Filyos river, turbidity, water quality

Proje Numarası

2015_37891158_02

Kaynakça

• Aksoy, B. 2018. Determination the effect of seasonal change on water quality of Filyos Stream by artificial neural network. Doctoral Thesis, Zonguldak Bulent Ecevit University, 267 s.
• Aslan, V. 2023. Bozova groundwater quality modeling and evaluation using fuzzy Ahp method based on GIS technique. Journal of Anatolian Environmental and Animal Sciences, 8(1):16-27. http://doi.org/10.35229/jaes.1201454
• Atıcı, AA. 2020. Determination of water quality characteristics of Dönerdere, Yumruklu, Değirmigöl and Dolutaş Ponds (Van, Turkey), Journal of Anatolian Environmental and Animal Sciences, 3(5):348-355. https://doi.org/10.35229/jaes.756835
• Bakar, C., Baba, A. 2009. Metals and human health: An environmental health problem inherited from the twentieth century to the present and the future. 1st Medical Geology Workshop,s. 162, Nevşehir, Turkey.
• Clesceri, LS., Greenberg, AE., Eaton, AD. 1998. Standard methods for the examination of water and wastewater. 20th Edition, American Public Health Association, Washington DC.
• Çıtakoğlu, H., Özeren, Y. (2021). Modeling of Sakarya basin water quality parameters with artificial neural networks. European Journal of Science and Technology, 24:10-17. https://doi.org/10.31590/ejosat.898046 Demirci, F. 2008. Analysis of sediment accumulation areas in Filyos basin with satellite image data and digital terrain model, Master's Thesis, Istanbul Technical University,85 s.
• Demirel, H. 2021. Accumulation effect of heavy metals in Sapanca Lake Basin and mass balance model, PhD Thesis, Sakarya University, 285 s.
• Eaton, AD., Franson, MAH., Clesceri, LS., Rice, EW., Greenberg, AE. 2005. Standard methods for the examination of water & wastewater. In Standard methods for the examination of water & wastewater, American Public Health Association, Washington DC, pp. 1-v.
• Elmas, Ç. 2003. Artificial Neural Networks (Çetin Elmas, Ed.). Ankara: Seçkin Publishing.
• Gürsoy Haksevenler, BH., Atasoy Aytış, E., Dilaver, M., Küçük, E., Pilevneli, T., Yetiş, Ü., … Şıltu, E. 2018. Natural background concentrations determination for metals in surface waters, Gediz River Basin. Turkish Journal of Water Science & Management, 3(1):2-21. https://doi.org/10.31807/tjwsm.355287.
• Kaçar, H., Yılmaz, S., Türkoğlu, M., Sadıkoğlu, M. 2022. Seasonal variations in tap water quality parameters in Çanakkale, Turkey. Turkish Journal of Analytical Chemistry, 4(1):6-18. https://doi.org/10.51435/turkjac.1111456
• Kaya, E. 2022. Evaluation of water quality parameters of Lake Iznik with artificial neural networks method. Master Thesis, Sakarya University, 110 s.
• Küçükali, S. 2019. Statistical investigation of physical water quality parameters of Filyos River. Journal of Anatolian Environmental and Animal Sciences, 4(3):519-524. https://doi.org/10.35229/jaes.636576.
• Leventeli, Y., Yalçın, F. 2019. Heavy Metal Pollutıon Index (Hpı) ın surface water between Alakır Dam and Alakır Brıdge, Antalya-Turkey. Kahramanmaraş Sütçü İmam University Journal of Engineering Sciences, 22:125-131.
• Oskay, V., Karagöz, Ö., Kuşlu, S. 2022. Investigation of water quality of the Şenkale stream feeding the Bahçecik dam meeting the drinking water needs of Gümüşhane province, Gumushane University Journal of Science and Technology, 61-75. https://doi.org/10.17714/gumusfenbil.1035164.
• Öztemel, E. 2016. Artificial neural networks. 3rd Edition, Papatya publishing, İstanbul, 232 s.
• Sonmez, AY., Kale, S., Ozdemir, RC., Kadak, AE. 2018. An adaptive neuro-fuzzy inference system (ANFIS) to predict of cadmium (Cd) concentrations in the Filyos River, Turkey. Turkish Journal of Fisheries and Aquatic Sciences, 18(12):1333-1343. http://doi.org/10.4194/1303-2712-v18_12_01