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Neural Computing and Applications

Erschienen in:

01.07.2014 | Original Article

Forecasting of monthly river flow with autoregressive modeling and data-driven techniques

verfasst von: Özlem Terzi, Gülşah Ergin

Erschienen in: Neural Computing and Applications | Ausgabe 1/2014

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Abstract

This study was conducted by using autoregressive (AR) modeling and data-driven techniques which include gene expression programming (GEP), radial basis function network and feed-forward neural networks, and adaptive neural-based fuzzy inference system (ANFIS) techniques to forecast monthly mean flow for Kızılırmak River in Turkey. The lagged monthly river flow measurements from 1955 to 1995 were taken into consideration for development of the models. The correlation coefficient and root-mean-square error performance criteria were used for evaluating the accuracy of the developed models. When the results of developed models were compared with flow measurements using these criteria, it was shown that the AR(2) model gave the best performance among all developed models and the GEP and ANFIS models had good performance in data-driven techniques.

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Titel: Forecasting of monthly river flow with autoregressive modeling and data-driven techniques
verfasst von: Özlem Terzi
Gülşah Ergin
Publikationsdatum: 01.07.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1469-9

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