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Artificial Neural Network Modeling for Predicting of some Ion Concentrations in the Karaj River
The water quality of the Karaj River was studied through collecting 2137 experimental data set gained by 20 sampling stations. The data included different parameters such as T (temperature), pH, NTU (turbidity), hardness, TDS (total dissolved solids), EC (electrical conductivity) and basic anion, cation concentrations. In this study a multi-layer perceptron artificial neural network model was designed to predict the calcium, sodium, chloride and sulfate ion concentrations of the Karaj River. 1495 data set were used for training, 321 data set were used for test and 321 data set were used for validation. The optimum model holds sigmoid tangent transfer function in the middle layer and three different forms of the training function. The root mean square error (RMSE), mean relative error (MRE) and regression coefficient (R) between experimental data and model’s outputs were measured for training, validation and testing data sets. The results indicate that the ANN model was successfully applied for prediction of calcium ion concentration.
Artificial Neural Network Modeling for Predicting of some Ion Concentrations in the Karaj River
The water quality of the Karaj River was studied through collecting 2137 experimental data set gained by 20 sampling stations. The data included different parameters such as T (temperature), pH, NTU (turbidity), hardness, TDS (total dissolved solids), EC (electrical conductivity) and basic anion, cation concentrations. In this study a multi-layer perceptron artificial neural network model was designed to predict the calcium, sodium, chloride and sulfate ion concentrations of the Karaj River. 1495 data set were used for training, 321 data set were used for test and 321 data set were used for validation. The optimum model holds sigmoid tangent transfer function in the middle layer and three different forms of the training function. The root mean square error (RMSE), mean relative error (MRE) and regression coefficient (R) between experimental data and model’s outputs were measured for training, validation and testing data sets. The results indicate that the ANN model was successfully applied for prediction of calcium ion concentration.
Artificial Neural Network Modeling for Predicting of some Ion Concentrations in the Karaj River
Kamyar Movagharnejad (author) / Alireza Tahavvori (author) / Forogh Moghaddam Ali (author)
2017
Article (Journal)
Electronic Resource
Unknown
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