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Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam
Author(s) -
Asadollahfardi Gholamreza,
Zangooei Hossein,
Aria Shiva Homayoun,
Danesh Elnaz
Publication year - 2017
Publication title -
environmental quality management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 27
eISSN - 1520-6483
pISSN - 1088-1913
DOI - 10.1002/tqem.21493
Subject(s) - artificial neural network , multilayer perceptron , normalization (sociology) , water quality , total dissolved solids , upstream and downstream (dna) , environmental science , sulfate , upstream (networking) , biological system , chemistry , computer science , environmental engineering , artificial intelligence , telecommunications , ecology , sociology , anthropology , biology , organic chemistry
We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca 2+ ), magnesium ions (Mg 2+ ), sodium ions (Na + ), chloride ions (Cl − ), sulfate ( SO 4 2 − ), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination ( R 2 ) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R 2 between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.