Abstract
The present study is aimed to assess the spatial variation of groundwater quality based on the influencing hydrogeological parameters in the surrounding mining areas of India’s one of the largest coal fields, the Korba coal field, Chhattisgarh, Central India. To achieve this goal, a knowledge-driven approach with the aid of a Machine Learning (ML) decision tree-based model, i.e., Classification and Regression Tree (CART) model, was developed to predict possible factors contributing to the degradation of groundwater quality in the selected regions. A total of five influencing factors were selected viz., water table depth (WTD), groundwater drawdown (DR), slope (S), elevation (E), and distance to mines (DTM), which were considered as the important input variables. Groundwater Quality Index (GWQI) values of 216 locations within a buffer zone of 20 km centered from the coal mining area were assigned as the target variables in the CART model. The influences of these factors on groundwater quality were assessed using a recursive partitioning combined with a pruned algorithm. Results showed that the significant factors followed decreasing trend of S (34%) > DTM (23%) > WTD (16%) > DR (15%) > E (12%). The model predicted relatively higher GWQI values attributed to the available lower ground slope in the study area. Similarly, wells situated within a 3 km radius of the buffer zone had low groundwater quality apparently due to the influence of mines. Higher GWQI values were observed in the wells having low WTD value (˂7.9 m) and higher DR in the study area. The results suggested that the anthropogenic activity is one of the major sources of groundwater contamination, whereas the impact of mines was only observed within a radius of 3 km from the center of the mining areas.
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Acknowledgements
Authors would like to sincerely thank the Editor and the anonymous reviewers for providing insightful suggestions for improving the quality of the manuscript. Authors sincerely acknowledge the help received from the Central Ground Water Board (CGWB), Raipur and Chhattisgarh Council of Science and Technology (CGCOST), Raipur for providing necessary data and information utilized in the present work.
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Singha, S.S., Singha, S., Pasupuleti, S. et al. Knowledge-driven and machine learning decision tree-based approach for assessment of geospatial variation of groundwater quality around coal mining regions, Korba district, Central India. Environ Earth Sci 81, 36 (2022). https://doi.org/10.1007/s12665-021-10147-1
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DOI: https://doi.org/10.1007/s12665-021-10147-1