Nüket SİVRİ, H. Kurtuluş ÖZCAN, O. Nuri UÇAN, Orkun AKİNCİLAR

Estimation of stream temperature in Degirmendere river (Trabzon- Turkey) using Artificial Neural Network model

Artificial Neural Networks (ANN) is a modeling technique with training which takes the working system of the brain as basis. Learning in ANNs is realized with the renewal of the connection gaps. ANNs make possible to solve the nondefined problems through the learning ability. In this study, we have estimated various environmental factors using an artificial model of the brain, known as Artificial Neural Network (ANN). Here, we has developed and tested an ANN model to predict stream temperature of Degirmendere in the Black Sea, using local water temperature, air temperature, and stream temperature. In the structure of the ANN used in the model, the number of the hidden neuron was determined as 16, Sum- Squared Error was determined as 0.005 and the number of the iteration was determined as 40,000. As a result of the regression analysis realized between the model outputs and measurement results obtained in the study, the value of r = 0.92 was calculated. When the other literature studies which had done before have been examined, in the light of the model outputs and statistical evaluations, and regarding the complex and nonlinear structure of the study environment, it was seen that the ANN modeling technique can be utilized in the timely prediction of the temperatures of the stream waters.

Anahtar Kelimeler:

çevresel faktörler, nehir suyu, benzetim modelleri, akarsular, Türkiye, su sıcaklığı, hava sıcaklığı

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Boran, M. and Karacam, H. 1996. The Seasonal variation in pollutants load on Degirmendere and Karadere Rivers. J. Fisheries Aquat. Sci., 3-4: 395-402.

Boyee, K.D. 1982. A guide to stream habitat analysis using the instream incremental methodology. Instream Flow Information Paper 12. U.S. Fish and Wildlife Service EWS/OBS-82/26. USA, 248 pp.

Boznar, M., Lesjak, M. and Mlakar, P. 1993. A neural network based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos Environ., 27B(2): 221-230.

Chelani, AB, Chalapati, C.V., Phadke, K.M. and Hasan, M.Z. 2002. Prediction of sulphur dioxide concentration using artificial neural networks. Env. Modeling&Software, 17: 161-168.

Duxbury, A.C. and Duxbury, A.B. 1994. An introduction to the world’s oceans, Wm. C. Brown Publishers.

Gardner, M.W. and Dorling, S.R. 2000. Statistical surface ozone models; an improved methodology to account for nonlinear behavior. Atmos. Environ., 34: 21-34.

Mullane, N., Sturdevant, D. and Baumgartner, R. 1995. 1992–1994 Water quality standards reviewtemperature final issue paper: Portland, Oregon. Oregon Department of Environmental Quality, Standards and Assessment Section, Portland, Oregon. 114 pp.

Nemerov, N.L. 1985. Stream, lake, estuary and ocean pollution, Van Nostrand, Reinhold Company, New York.

Ozcan, H.K., Ucan, O.N., Sahin, U., Borat, M. and Bayat, C. 2006. Artificial neural network modeling of methane emissions at Istanbul Kemerburgaz-Odayeri landfill site, Journal of Scienfitic and Industrial Research, 65(2): 128-134.

Quilly, E.P. and Hudson, T. 2004. Artificial neural networks for validation and correction of high frequency water quality data, 11th CWWA National Conference in Calgary, April 3-6.

Risley, J.C. 2000. Effects of hypothetical management scenarios on simulated water temperatures in the Tualatin River, Oregon. Water-Resources Investigations Report, 00–4071.

Rounds, S.A. 2002. Development of a neural network model for dissolved oxygen in the Tualatin River, Oregon. Proceeding of the Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, USA.

Saila, S.M. and Cheeseman, D. 2004. Maximum stream temperature estimation from air temperature data and its relationship to brook trout (Salvelinus fontinalis) habitat requirements in Rhode Island. Wood- Pawcatuck Watershed Association (WPWA).

Sivri, N., Kose, E. and Feyzioglu, A.M. 2006. Modeling the effects of industrial discharged waters to Degirmendere River (South Eastern Black Sea). Journal of Scienfitic and Industrial Research, 65(8): 632-639.

Sivri, N., Kilic, N. and Ucan, O.N. 2007. Estimation of stream temperature in Firtina Creek (Rize-Turkiye) using artificial neural network model. J. Environ. Biol., 28(01): 67-72.

Soyupak, S., Karaer, F., Gürbüz, H., Kivrak, E., Sentürk, E. and Yazici, A. 2003. A neural network based approach for calculating dissolved oxygen in reservoirs. Neural Comput & Application, 12: 166-172.