A comparative assessment of adaptive neuro-fuzzy inference system, artificial neural network and regression for modelling stage-discharge relationship. (2016)

• Record Type: Journal Article
• Authors: Vafakhah, Mehdi; Kahneh, Ebrahim
• Is Part Of: International journal of hydrology science and technology. Volume 6:Number 2(2016)
• Page Start: 143
• Page End: 159
• Publication Date: 2016
• Subjects: artificial neural networks -- ANNs: adaptive neuro-fuzzy inference system -- ANFIS -- multi-linear regression -- stage-discharge relationship -- flow discharge -- rating curve -- fuzzy logic -- Iran -- hydrology -- water discharge -- river discharge -- modelling; Hydrology -- Periodicals; Water resources development -- Periodicals; Hydrology; 553.705
• Languages: English
• ISSNs: 2042-7808

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.