Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is give...

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Bibliographic Details
Published in:Hydrological sciences journal 2006-08, Vol.51 (4), p.588-598
Main Authors: KESKIN, M. EROL, TAYLAN, DILEK, TERZI, ÖZLEM
Format: Article
Language:English
Subjects:
ANFIS modelling technique
ARMA models
Earth sciences
Earth, ocean, space
Exact sciences and technology
flow prediction
fuzzy systems
Hydrology
Hydrology. Hydrogeology
modèles ARMA
neural networks
prévision d'écoulement
réseaux de neurones
systèmes flous
technique de modélisation ANFIS
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Summary:The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS.
ISSN:0262-6667
2150-3435
DOI:10.1623/hysj.51.4.588