reconstruction of daily discharge using artificial neural network and neuro-fuzzy methods (case study: upstream of karoun watershed)

daily constant discharges are needed estimating daily discharge in the hydrological model. the different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. hence the reconstruction of daily discharge data is of paramount importance. in this research, daily discharge was reconstructed in two stages in one of the upstream of karoun river. in both stages of research, daily discharge data from two upstream stations were used to reconstruct daily discharge of the downstream station using artificial neural networks, neuro-fuzzy and two variables regression methods. in the second stage, the magnitudes of discharge, based on dry, normal and wet years was used to reconstruct the daily discharge. the results showed higher accuracy in the artificial neural network and neuro-fuzzy methods compared to two variable regression methods in the reconstruction of daily discharge. multi-layer perceptron model has better potential among all different method of artificial neural network and neuro-fuzzy models. classification of discharge into dry, normal, and wet years decreases error in the reconstruction of daily discharge. based on the mean relative error (mre), error in reconstruction of daily discharge is the least in normal, wet, and dry years, respectively.