Application of Intelligent Neural Models in Determining the Water Quality Parameters of Dams Reservoirs (Case study: Ekbatan dam of Hamadan)

Identifying the quantitative and qualitative problems in water resources monitoring systems is one of the most important steps in formulating the structure of water resources systems management plans and implementing pollution reduction environmental plans. It is considered to use some indirect methods to simulate qualitative parameters in high volume in order to reduce cost, time and high accuracy. In the field of simulating water quality models, many models have been developed that require a lot of input parameters such as hydrological, meteorological data, etc., which require spending time and money to access them. The increasing expansion of computers and the use of artificial intelligence and the use of artificial neural network methods have been widely used in the estimation of qualitative parameters.

In the present study, based on the ability of intelligent neural models of four models such as artificial neural network, neuro-fuzzy, neural-wavelet, and neuro-fuzzy-wavelet were used to predict the water quality parameters of the Hamadan Ekbatan dam. For this purpose, BOD5, DO, pH, temperature, total solids and water turbidity were measured during 1388and 1389 to estimate phosphate, nitrate, fecal coliform and total chlorophyll a. in order to evaluation of the environmental conditions on the accuracy of the results , predictions were made in the last two warm and cold periods of the year .

Based on the results, the combined model of neural network with wavelet theory was introduced as the optimal structure for estimating all four qualitative parameters in both periods. Among the parameters studied during the warm period, the lowest normal root mean square error (NRMSE) and the highest correlation coefficient were 0.990 and 0.999, Furthermore, in the cold period, the least amount of NRMSE and the most correlation coefficient was 2.75 and 0,905 have seen for the nitrate quality parameter In this research, 4 smart models were used to predict the quality parameters of phosphate, nitrate, faecal coliform and coliform a of Ekbatan Dam in Hamedan. The error measurement criteria of accuracy and agreement with the measured value were examined. In general, the combined method of neural network and wavelet theory was introduced as an optimal structure with a high correlation coefficient and a lower error rate than other methods for both cold and warm periods. The predicted value using this method has the most agreement with the measured value. Using smart models reduces cost and time and has high accuracy. Also, the accuracy of forecasting of qualitative parameters is more in hot period than in cold period.In this research, 4 smart models were used to predict the quality parameters of phosphate, nitrate, faecal coliform and coliform a of Ekbatan Dam in Hamedan. The error measurement criteria of accuracy and agreement with the measured value were examined. In general, the combined method of neural network and wavelet theory was introduced as an optimal structure with a high correlation coefficient and a lower error rate than other methods for both cold and warm periods. The predicted value using this method has the most agreement with the measured value. Using smart models reduces cost and time and has high accuracy. Also, the accuracy of forecasting of qualitative parameters is more in hot period than in cold period.In this research, 4 smart models were used to predict the quality parameters of phosphate, nitrate, faecal coliform and coliform a of Ekbatan Dam in Hamedan. The error measurement criteria of accuracy and agreement with the measured value were examined. In general, the combined method of neural network and wavelet theory was introduced as an optimal structure with a high correlation coefficient and a lower error rate than other methods for both cold and warm periods. The predicted value using this method has the most agreement with the measured value. Using smart models reduces cost and time and has high accuracy. Also, the accuracy of forecasting of qualitative parameters is more in hot period than in cold period.In this research, 4 smart models were used to predict the quality parameters of phosphate, nitrate, faecal coliform and coliform a of Ekbatan Dam in Hamedan. The error measurement criteria of accuracy and agreement with the measured value were examined. In general, the combined method of neural network and wavelet theory was introduced as an optimal structure with a high correlation coefficient and a lower error rate than other methods for both cold and warm periods. The predicted value using this method has the most agreement with the measured value. Using smart models reduces cost and time and has high accuracy. Also, the accuracy of forecasting of qualitative parameters is more in hot period than in cold period.In this research, 4 smart models were used to predict the quality parameters of phosphate, nitrate, faecal coliform and coliform a of Ekbatan Dam in Hamedan. The error measurement criteria of accuracy and agreement with the measured value were examined. In general, the combined method of neural network and wavelet theory was introduced as an optimal structure with a high correlation coefficient and a lower error rate than other methods for both cold and warm periods. The predicted value using this method has the most agreement with the measured value. Using smart models reduces cost and time and has high accuracy. Also, the accuracy of forecasting of qualitative parameters is more in hot period than in cold period.In this research, 4 smart models were used to predict the quality parameters of phosphate, nitrate, faecal coliform and coliform a of Ekbatan Dam in Hamedan. The error measurement criteria of accuracy and agreement with the measured value were examined. In general, the combined method of neural network and wavelet theory was introduced as an optimal structure with a high correlation coefficient and a lower error rate than other methods for both cold and warm periods. The predicted value using this method has the most agreement with the measured value. Using smart models reduces cost and time and has high accuracy. Also, the accuracy of forecasting of qualitative parameters is more in hot period than.