Interactive approach for determination of salinity concentration in tidal rivers (Case study: The Karun River in Iran)

In this research, a perceptron artificial neural network is trained and validated by a number of observed data. Inputs of artificial neural network (ANN) are distance from upstream, discharge of freshwater at upstream and tidal height at downstream and its output is salinity concentration. Because of shortage of observed data especially in extreme conditions, a numerical model was developed. This model was calibrated by observed data. Results of numerical model convert to two regression relations. Then artificial neural network is tested by reminder observed data and results of numerical model. For improving of results of test of ANN, it is trained by genetic algorithm (GA) method. GA method decreases the mean of square error (MSE) 66.4% and increases efficiency coefficient 3.66%. Sensitivity analysis shows that distance from upstream is the most effective governing factor on salinity concentration. For case study, the Karun River in south west of Iran is considered. (C) 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).