A Comparison of Statistical and Intelligent Methods for Estimating River Salinity: A Case Study of Simineroud, Western Azerbaijan, Iran

Salinity (EC) is an important factor, especially in the river engineering and water quality studies. In recent decades, the artificial intelligent systems such as artificial neural networks have many applications in various researches, including water resources management. In this study, water electrical conductivity was estimated using artificial neural networks for Simineh-Rood, West Azerbaijan province, Iran. After that, the result were compared with statistical results using conventional methods such as multivariate linear regression. Hydrogen carbonate, chloride sulfate, and the flow rate in the monthly time scale of the period (1992- 2009) were used as the input for linear regression and EC was considered as the output. Criteria of correlation coefficient, root mean square error and coefficient of Nash Sutcliff were used as evaluating criteria for model performance. The results showed that the artificial neural network model has the highest correlation coefficient (0.967), the lowest root mean square error (0.047ds/m) and the Nash Sutcliff criteria (0.706). Overall, the results showed that the artificial neural network model can be used for estimating minimum and maximum rates of water salinity.