Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model)Case of study: Jajroud River of Tehran and Gharaso River of Kermanshah)

Rivers are the most important resources supplying drinking, agricultural, and industrial water demand. Their quality fluctuates frequently due to crossing from different regions and beds as well as their direct relationship with their peripheral environments. Thus, it is essential to be considered the surveying and predicating changes in the water qualitative parameters in a river. In this study, in order to estimate some of the qualitative parameters (Total dissolved solids, electrical conductivity and sodium absorption rate) for Tehran Jajroud and Kermanshah Gharasu rivers, we used wavelet-artificial neural network (W-ANN) hybrid model during a statistical period of 24 years. We compared W-ANN model with ANN model in order to evaluate its capability in detecting signals and separating error signals for estimating water quality parameters of the abovementioned rivers. The evaluation of both models was performed by the statistical criteria including correlation coefficient, the Nash-Sutcliffe model efficiency coefficient (NS), the root mean square error (RMSE) and the mean absolute error (MAE). The results showed that the optimized W-ANN with correlation coefficient of 0.9 has high capability to estimate SAR parameter in the stations studied. Moreover, we found that W-ANN had less error and higher accuracy in the case of EC and TDS parameters rather than ANN model. W-ANN proved high efficiency in forecasting of the water quality parameters of rivers, therefore, it can be used for decision making and assurance of monitoring results and optimizing the monitoring costs.