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An Adaptive Neuro-Fuzzy Inference System (ANFIS) to Predict of Cadmium (Cd) Concentrations in the Filyos River, Turkey      
Yazarlar
Adem Yavuz Sönmez
Kastamonu Üniversitesi, Türkiye
Semih Kale
Çanakkale Onsekiz Mart Üniversitesi, Türkiye
Arş. Gör. Rahmi Can ÖZDEMİR
Kastamonu Üniversitesi, Türkiye
Doç. Dr. Ali Eslem KADAK
Kastamonu Üniversitesi, Türkiye
Özet
Water quality is one of the main characteristics of a river system and prediction of water quality is the key factor in water resource management. Different physical, biological and chemical parameters including heavy metals can be used to assess river water quality. Evaluation of the water quality in the rivers is quite difficult and requires more time and effort because of the fact that many factors affect water quality. Traditional data processing methods are insufficient to solve this problem. Therefore, in this study, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the concentrations of cadmium (Cd) in the Filyos River, Turkey. For this purpose, water samples collected at 7 sampling locations in the river during December 2014-2015 were used to develop ANFIS model. The available data set was apportioned into two separate sections for training and testing the ANFIS model. Developed models aimed to use the least parameters to estimate Cd concentration. As a result, a relatively higher correlation (R2=0.91) was found between observed and modelled Cd concentrations. The results indicated that the ANFIS model gave reasonable estimates for the concentrations of Cd with a high degree accuracy and robustness. In conclusion, this paper suggests that ANFIS methodology produce very successful findings and has the ability to predict Cd concentration in water resources. The outcomes of this research provide more information, simulation, and prediction about heavy metal concentration in natural aquatic ecosystems. Therefore, ANFIS can be used in further researches on water quality monitoring.
Anahtar Kelimeler
ANFIS,cadmium,heavy metal
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı Turkish Journal of Fisheries and Aquatic Sciences
Dergi ISSN 1303-2712
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q3
Makale Dili İngilizce
Basım Tarihi 01-2018
Cilt No 18
Sayı 12
Doi Numarası 10.4194/1303-2712-v18_12_01
Makale Linki http://www.trjfas.org/pdf/issue_18_12/1201.pdf