Application of an Artificial Neural Network Model to Rivers Water Quality Indexes Prediction – A Case Study

Taxonomic Recent trends in the management of water supply have increased the need for modeling techniques that can provide reliable, efficient, and accurate representation of the nonlinear dynamics of water quality within water distribution systems. Since artificial neural networks have been widely applied to the nonlinear transfer function approximation, in this study we present an empirical multi layer perceptron neural network to estimate water quality indexes (BOD, Do) in Morad Big River in the western part of Iran. In this paper, the information and data including 10 monthly parameters of water quality in the Hamedan Morad Big River in duration of one year and six stations were used for modeling biological oxygen demanded (BOD) and dissolved oxygen (DO) as indices affecting water quality. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. Performance of the model was evaluated by statistical criteria includes correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE). In the optimum structure of neural network correlation coefficient for BOD and DO are 0.986 and 0.969, also root mean square error are 8.42 and 0.84 respectively. The results show the identified ANN’s great potential to simulate water quality variables. [Hossein Banejad, Ehsan Olyaie. Application of an Artificial Neural Network Model to Rivers Water Quality Indexes Prediction – A Case Study. Journal of American Science 2011;7(1):60-65]. (ISSN: 1545-1003). http://www.americanscience.org.