Groundwater Quality Assessment and Prediction of Spatial Variations in the Area of the Danube River Basin (Serbia)

Abstract

Monitoring and forecasting of chemical and physicochemical parameters of groundwater is an important factor in quality control and water management. In order to optimize these processes, the initial purpose of this paper was to identify sources of pollution and predict spatial changes in groundwater quality. Patterns of spatial changes in groundwater quality in the Danube river basin (Serbia) have been identified using multivariate statistical techniques. The results of the applied cluster analysis are the indicators of the existance of two spatial clusters. The principal component/factor analysis (PCA/FA) has shown that beside natural, anthropogenic factor has an influence in spatial grouping. Discriminant analysis (DA) was applied in order to identify discriminant groundwater quality parameters. DA reduced the number of data by extracting two parameters (iron and arsenic). The spatial distribution of identified dominant factors and discriminant parameters were graphically represented using GIS. Finally, the artificial neural network technique was used to test the ability to predict spatial changes in the values of discriminant parameters, and the reliability of this technique to predict the spatial variations of the two extracted variables has been proven.