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

• Published in: Water, air, and soil pollution air, and soil pollution, 2021-03, Vol.232 (3), Article 117
• Main Authors: Ilic, Ivana, Puharic, Mirjana, Ilic, Dejan
• Format: Article
• Language: English
• Subjects: Analysis; Anthropogenic factors; Arsenic; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Climate Change/Climate Change Impacts; Cluster analysis; Discriminant analysis; Earth and Environmental Science; Environment; Environmental monitoring; Factor analysis; Geographical information systems; Graphical representations; Groundwater; Groundwater quality; Human influences; Hydrogeology; Identification; Management; Neural networks; Parameter identification; Parameters; Physicochemical processes; Physicochemical properties; Pollution sources; Principal components analysis; Quality assessment; Quality control; River basins; Rivers; Soil Science & Conservation; Spatial analysis; Spatial distribution; Spatial variations; Statistical analysis; Water; Water management; Water pollution; Water quality; Water Quality/Water Pollution; Water, Underground
• ISSN: 0049-6979; 1573-2932
• DOI: 10.1007/s11270-021-05069-4

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.