A comparative assessment of adaptive neuro-fuzzy inference system, artificial neural network and regression for modelling stage-discharge relationship
by Mehdi Vafakhah; Ebrahim Kahneh
International Journal of Hydrology Science and Technology (IJHST), Vol. 6, No. 2, 2016

Abstract: This study aims to evaluate the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), two and multi-linear regression (MLR) for modelling stage-discharge (S-D) relationship and the results are compared with those of the S-D rating curves. S-D relationship was carried out using discharge and water level (stage) data during 2011-2012 water year in Tangeh Lavij hydrometry station with area of 104 km² located in Mazandaran Province. The partial autocorrelation coefficients were estimated for the daily stage and three input combinations based on current and previous daily stage were selected as inputs to the ANFIS, ANN and MLR models. ANN, ANFIS and MLR methods with three input combinations of stage and conventional S-D relationship were used for modelling S-D relationship. The results showed that artificial intelligence methods were found to be superior to regression and conventional S-D relationship and ANFIS are the most suitable method for modelling S-D relationship.

Online publication date: Mon, 28-Mar-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Hydrology Science and Technology (IJHST):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com