How to cite: M. DEMIRCI , F. UNES, Y. Z. KAYA, B. TASAR, H. VARCİN (2018) Modeling of dam reservoir volume using adaptive neuro fuzzy method.
2018 Air and Water Components of the Environment Conference Proceedings, P. 145-152, DOI: 10.24193/AWC2018_18

 

MODELING OF DAM RESERVOIR VOLUME USING ADAPTIVE NEURO FUZZY METHOD

M. DEMIRCI , F. UNES, Y. Z. KAYA, B. TASAR, H. VARCİN

DOI: 10.24193/AWC2018_18

ABSTRACT. Modeling of dam reservoir volume using adaptive neuro fuzzy method. Dam reservoir capacity estimation are important for dam structures, operation, design and safety assessments. Predictions of reservoir volumes must be considered as one of the main part of water resources management. As it is known in water resources management, reservoir capacity has direct effects on choosing irrigation systems, energy production, water supply systems etc. in a study region. In this study, the reservoir capacity of the Stony Brook dam in the USA state of Massachusetts, was tried to be estimated. Data set is taken by U.S. Geological Survey Institute (USGS) website. Reservoir capacity was estimated by Adaptive Neuro Fuzzy (NF) and Multilinear Linear Regression Analysis (MLR). NF model results was compared with MLR results. For the comparison, Mean Square Error (MSE), Mean Absolute Error (MAE) and correlation coefficient statistics were used.

Keywords: Lake level, Prediction, Neuro Fuzzy (NF), Support Vector Machines (SVMs).

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