River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches

Hadi Sanikhani () and Ozgur Kisi

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2012, vol. 26, issue 6, 1715-1729

Abstract: This paper demonstrates the application of two different adaptive neuro-fuzzy (ANFIS) techniques for the estimation of monthly streamflows. In the first part of the study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly flow data from two stations, the Besiri Station on the Garzan Stream and the Baykan Station on the Bitlis Stream in the Firat-Dicle Basin of Turkey were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. In the second part of the study, the performance of the ANFIS techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the performance of the ANFIS-SC model was slightly better than the ANFIS-GP model in streamflow forecasting. Copyright Springer Science+Business Media B.V. 2012

Keywords: Estimation; Forecasting; Adaptive neuro-fuzzy techniques; Streamflow (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (22)

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DOI: 10.1007/s11269-012-9982-7

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