Stage-discharge modelling at Gaula barrage site using different soft computing techniques
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Date
2021-02
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
Development of stage-discharge relationship is extremely important issue in hydrological modelling. Due to complexity of stage-discharge relationship, the discharge prediction plays an important role for planning and management of water resources. Considering these facts, a study has been carried out for modelling of discharge at Gaula barrage site. The Gaula barrage site is located in Uttarakhand, India and it is 578km long river. In the present study, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and wavelet based artificial neural system (WANN) based models were used to estimate the discharge for study area. The daily data for monsoon period (1st June to 30th September) of 1024 days and 440 days were used to train and test the models, respectively. The Gamma test was carried out to identify the best model for discharge prediction. The input data having stage with one day lag and discharge with one and two days lag and current day discharge as output were used for discharge modelling. In case of ANN models, back-propagation algorithm and hyperbolic tangent sigmoid activation function were used. WANN used Haar a trous based wavelet function while in ANFIS models, triangular, psig, generalized bell and gaussian membership functions were used to train and test the models. The performance of the models was evaluated qualitatively by visual observation and quantitatively using correlation coefficient, root mean square error, Willmott index and coefficient of efficiency. It was found that ANFIS based model performed better than ANN and WANN based models for discharge prediction at Gaula barrage site.