An artificial neural networks approach to monthly flow estimation

In this study, flow estimation models has been developed with artificial neural networks (ANN) method which is one of the artificial intelligence techniques. The various ANN models are developed by using different input combinations consisting of flow data of Söğütlühan (1535), Yamula (1501) and...

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Bibliographic Details
Published in2011 International Symposium on Innovations in Intelligent Systems and Applications pp. 325 - 328
Main Authors Ilker, A., Kose, M., Ergin, G., Terzi, O.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Artificial neural networks
Data models
estimation
flow
Fluid flow measurement
Forecasting
Kmhrmak
Neurons
Rivers
Training
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Summary:In this study, flow estimation models has been developed with artificial neural networks (ANN) method which is one of the artificial intelligence techniques. The various ANN models are developed by using different input combinations consisting of flow data of Söğütlühan (1535), Yamula (1501) and Bulakbaşi (1539) stations and compared with measured monthly flow. The coefficient of determination (R 2 ) and root mean square error (RMSE) are used as performance criteria to compare results. According to these criteria, the model which has flow values in t-1 time of 1501, 1539 and 1535 stations, and in t time of 1501 and 1539 stations has the highest R 2 of 0.98 and 0.97 for training and testing set, respectively.
ISBN:9781612849195
1612849199
DOI:10.1109/INISTA.2011.5946110