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

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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.

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Acknowledgements

The work was financially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No, 451-03-68/2010-14/200026). The authors would also like to thank the Faculty of Information Technology and Engineering, University UNION-Nikola Tesla, Belgrade, for their support and cooperation.

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Authors and Affiliations

  1. Faculty of Information Technology and Engineering, University UNION-Nikola Tesla, Belgrade, Serbia

    Ivana Ilić & Mirjana Puharić

  2. Faculty for Business Studies and Law, University UNION-Nikola Tesla, Belgrade, Serbia

    Dejan Ilić

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  1. Ivana Ilić
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Correspondence to Ivana Ilić.

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Highlights

• Cluster analysis grouped measuring locations in two clusters (cluster 1 included 16 measuring spots and cluster 2 seven measuring spots).

• Application of multivariate statistical techniques in order to identify dominant factors and discriminant parameters.

• PCA/FA for identified seven dominant factors for cluster 1 and five for cluster 2.

• Application of discriminant analysis identified two discriminant parameters.

• GIS interpolation technique—IDW enabled creating spatial distribution maps for dominant factors (for both clusters) and discriminant parameters.

• Application of artificial neural networks (ANNs) enabled predicting spatial varifactors.

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Ilić, I., Puharić, M. & Ilić, D. Groundwater Quality Assessment and Prediction of Spatial Variations in the Area of the Danube River Basin (Serbia). Water Air Soil Pollut 232, 117 (2021). https://doi.org/10.1007/s11270-021-05069-4

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