This paper describes the development of a model based on artificial neural networks (ANN) which aims to predict the concentration of nitrates in river water. Other 26 water quality parameters were also monitored and used as input parameters. The models were trained and tested with data from 10 monitoring stations at the Danube River, located in its course through Serbia, for the period from 2011 to 2016. Multi layer perceptron, standard three-layer network is used to develop models and two Input Variable Selection techniques are used to reduce the number of input variables. The obtained results have shown the ability of ANN to predict the nitrate concentration in both developed models with a value of mean absolute error of 0.53 and 0.42 mg/L for test data. Also, the application of IVS has contributed to reduce the number of input variables and to increase the performance of the model, especially in the case of Variance Inflation Factor (VIF) analysis where the estimation of multicollinearity among variables and the elimination of excessive variables significantly influenced prediction abilities of ANN model, r-0.91.


  • health assessment.

  • heavy metals in river water.

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