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Water Resources Management

Erschienen in:

01.04.2012

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

verfasst von: Hadi Sanikhani, Ozgur Kisi

Erschienen in: Water Resources Management | Ausgabe 6/2012

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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.

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Titel: River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches
verfasst von: Hadi Sanikhani
Ozgur Kisi
Publikationsdatum: 01.04.2012
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 6/2012
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-012-9982-7

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