Determination of Turbidity in Filyos Stream Water by Artificial Neural Network

Yıl 2019, Cilt: 3 Sayı: 1, 67 - 72, 27.06.2019

Berna Aksoy , İsmail Hakkı Özölçer , Emrah Doğan , Onur Dündar

Öz

Water
is in an endless cycle, which is source of life for human beings. During this
cycle, substances that are contaminated in water cause physical, chemical or
biological alteration of the water’s natural features, that leads to water pollution and therefore
causes the environmental balance to deteriorate over time. This quality changes
cause deteriorations in ecosystem. 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 downstream of the Filyos stream, which forms the largest
sub-basin in the Western Karadeniz Basin, at intervals of thirty days in one
year period between September 2015 and August 2016. In the scope of the study,
zinc, chromium, calcium, aluminium, manganese and turbidity parameters measured
in the laboratory and estimation of the turbidity parameter basaed on
parameters of zinc, chromium, calcium, aluminium, manganese was performed by
artificial neural networks.

Anahtar Kelimeler

Filyos Stream, Heavy Metal, Turbidity, Artificial Neural Networks

Kaynakça

• [1]. APHA Standart Methods for the Examination of Water and Waste Water, 20th Ed., APHA, AWWA, WEF, Washington , D. C., 1998.
• [2]. Ay M, Su Kalitesi Parametrelerinin Yapay Zeka Yöntemleri İle Değerlendirilmesi, Doktora Tezi, Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Anabilim Dalı, Kayseri, 117, 2014.
• [3]. Bayram A, Harşit Çayı Su Kalitesinin Mevsimsel Değişiminin İncelenmesi Ve Askı Madde Konsantrasyonunun Yapay Sinir Ağları Yöntemi İle Tahmin Edilmesi, Doktora, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Anabilim Dalı, Trabzon, 163, 2011.
• [4]. Bayram A ve Kenanoğlu M, Temporal Variation of Total Nitrogen and Total Phosphorus in Surface Waters from the Lower Çoruh River Basin, Turkey, 3rd International Conference on Computational and Experimental Science and Engineering, 19-24 Ekim 2016, Antalya, Türkiye, 132(3): 712-716, 2017.
• [5]. Doğan E, Şengörür B ve Köklü R, Modeling Biological Oxygen Demand of the Melen River in Turkey Using An Artificial Neural Network Technique, Journal of Environmental Management, 90, (2): 1229-1235, 2009.
• [6]. İçağa Y, Bostanoğlu Y ve Kahraman E, Akarçay Havzası Su Kalitesi İstatistikleri. Yapı Teknolojileri Elektronik Dergisi, 2(1): 43-50, 2006.
• [7]. Kajiya T, Schellenberger F, Papadopulos P, Vollmer D and Butt H J, 3D Imaging of Water-Drop Condensation on Hydrophobic and Hydrophilic Lubricant-Impregnated Surfaces. Nature, 6: 1-10, 2016.
• [8]. Minarecioğlu N, Doğal Akarsularda Taşınan Katı Madde Miktarının Yapay Zeka Yöntemleri Kullanılarak Tahmin Edilmesi, Yüksek Lisans, Erciyes Üniversitesi, Fen Bilimleri, İnşaat Mühendisliği Anabilim Dalı, Kayseri, 68, 2008.
• [9]. Öztemel E, Yapay Sinir Ağları, 3. Baskı, ISBN: 9756797396, Papatya Bilim, İstanbul, 232,2016.
• [10]. Sönmez A Y, Hisar O ve Yanık T, Karasu Irmağında Ağır Metal Kirliliğinin Tespiti ve Su Kalitesine Göre Sınıflandırılması. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 43(1): 69-77, 2012.