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Groundwater quality assessment in Bihar's aquifers: a machine learning approach.

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Title: Groundwater quality assessment in Bihar's aquifers: a machine learning approach.
Authors: Kumar P; Department of Civil Engineering, GITAM (Deemed to be University), Hyderabad, 502329, India., Singha SS; Department of Civil Engineering, KG Reddy College of Engineering and Technology, Hyderabad, 501504, India., Singha S; Department of Civil Engineering, GITAM (Deemed to be University), Hyderabad, 502329, India. ssingha@gitam.edu.
Source: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2026 May; Vol. 33 (18), pp. 8792-8819. Date of Electronic Publication: 2026 May 26.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
Imprint Name(s): Publication: <2013->: Berlin : Springer
Original Publication: Landsberg, Germany : Ecomed
MeSH Terms: Groundwater*/chemistry , Boosting Machine Learning Algorithms* , Environmental Monitoring* , Water Quality*, India ; Random Forest ; Water Pollutants, Chemical
Abstract: Globally, the primary concern affecting the suitability of groundwater for drinking is the presence of numerous chemical contaminants in large-scale aquifer systems. Therefore, it is essential to establish reliable methods for assessing groundwater quality and determining the origin of groundwater contaminants. This study developed a comprehensive, data-driven method for evaluating the quality of large-scale groundwater in the State of Bihar, India, using a traditional Water Quality Index (WQI) and a statistically based Root Mean Square Water Quality Index (RMS-WQI). In the present study, four state-of-the-art machine learning algorithms, namely, Classification and Regression Tree (CART), Light Gradient Boosting Model (LGBM), Random Forest (RF), and Extreme Gradient Boosting Model (XGBoost), were evaluated to assess their utility in predicting groundwater quality. Of the four models tested, XGBoost demonstrated the highest degree of predictive performance, exhibiting high levels of accuracy in terms of R2 values of 0.984 for the WQI and 0.994 for the RMS-WQI and low error metrics. Spatial diagnostics of the RMS-WQI model employing the Nash-Sutcliffe Efficiency (NSE), Model Efficiency Factor (MEF), and Percent Relative Error Index (PREI) identified heterogeneity in model performance, particularly in the data-volatile Gaya District, where NSE = -0.1. The uncertainty and robustness of the ML model were thoroughly evaluated using Monte Carlo simulations, which showed a reliability of 88.5%. Geochemical analysis indicated that both natural geochemical and anthropogenically influenced processes contributed to the variability in groundwater chemistry. Four main contributors to groundwater chemistry were identified through absolute principal component scores-multiple linear regression (APCS-MLR): mineral dissolution (32.7%), water-rock interactions (20.1%), mixed sources (16.3%), and anthropogenic inputs (13.2%). This innovative integrated methodology provides a scalable and cost- effective decision-making tool for predicting the spatial distribution of groundwater quality and supports the development of sustainable hydro-environmental management practices, while also supporting the achievement of United Nations Sustainable Development Goal 6.
(© 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Competing Interests: Declarations. Ethical approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable.
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Contributed Indexing: Keywords: Bihar; Groundwater; India; ML models; Monte-Carlo simulation; Water quality index
Substance Nomenclature: 0 (Water Pollutants, Chemical)
Entry Date(s): Date Created: 20260526 Date Completed: 20260612 Latest Revision: 20260623
Update Code: 20260623
DOI: 10.1007/s11356-026-37867-w
PMID: 42189468
Database: MEDLINE
Description
ISSN:1614-7499
DOI:10.1007/s11356-026-37867-w