Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube

Abstract Dissolved oxygen content is one of the most important parameters in the characterization of surface water conditions. Our goal is to make a forecast of this parameter in Central Europe’s most important river with the use of other, easily measurable water quality parameters (pH, temperature, electrical conductivity and runoff) with the use of linear and nonlinear models. We adapt four models for forecasting dissolved oxygen concentration, namely a Multivariate Linear Regression model, a Multilayer Perceptron Neural Network, a Radial Basis Function Neural Network and a General Regression Neural Network model. Data is available for Hungarian sampling locations on River Danube (Mohacs, Fajsz and Győrzamoly) for the period of 1998–2003. The analysis was performed with four alternative combinations, the models were formulated using data from the period 1998–2002 and a dissolved oxygen forecast was made for 2003. Evaluating model performance with various statistical measures (root mean square error, mean absolute error, coefficient of determination, and Willmott’s index of agreement), we found that non-linear models gave better results than linear models. In two cases the General Regression Neural Network provided the best performance, in two other cases the Radial Basis Function Neural Network gave the best results. A further goal was to conduct a sensitivity analysis in order to identify the parameter with the highest influence on the performance of the created models. Sensitivity analysis was performed for the combination of all three sampling locations (4th combination) and it was found that for all three neural network models sensitivity analyses showed that pH has the most important role in estimating dissolved oxygen content.