Abstract
Groundwater, the main source of drinking and irrigation water in developing countries, plays an important role in maintaining public health and crop production. However, groundwater quality is often compromised and geological aquifers that contain this valuable resource are at risk of contamination such as arsenic and emerging pollutants in the North 24-Parganas district of India. Monitoring of aquifer vulnerability is generally done by modeling which subjects to errors that must be taken into account. For an emerging vulnerability model DRASTIC, prone to subjectivity errors, three modification methods: statistical modification, DRASTIC-AHP, and DRASTIC-CNN, are used to identify the most effective strategy for reducing such subjectivity errors in this study. The model results are validated against the composite groundwater pollution index. The DRASTIC-CNN method outperforms other modifications, with the correlation coefficient increasing from 0.226 to 0.9 compared to the conventional DRASTIC model. The coefficient of determination for DRASTIC-CNN improves from 0.05 to 0.81, while the kappa statistic underscores its superiority in identifying vulnerable regions. Although statistical modification and DRASTIC-AHP demonstrate respectable performances, they are lower than the accuracy achieved by DRASTIC-CNN. DRASTIC-CNN emerges as the most efficient approach to reduce subjective errors in modeling aquifer vulnerability. This study accurately identifies groundwater vulnerability, helping policymakers to formulate sustainable management plans and mitigation measures for water purification. It importantly addresses environmental problems such as arsenic contamination and emerging pollutants. Integrating the DRASTIC model with other factors allows us to assess vulnerability and pave the way for sustainable treatment technologies.
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Funding
Authors would like to thank AICTE for providing fellowship under the AICTE-ADF scheme. The authors would also acknowledge the Science and Engineering Research Board, Department of Science and Technology, Government of India for funding the study under the Start-Up Research Grant (SRG/2021/002021).
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All authors have contributed to the conceptualization and design of the study and to writing and reviewing the final manuscript. Nilanjan Saha and Sekh Mohammad Hidayetullah have conducted field visits and sample testing. Rijurekha Dasgupta has executed data analysis. Final review and approval for submission is conducted by Prof. (Dr.) Asis Mazumdar, Dr. Subhasish Das and Dr. Gourab Banerjee.
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Dasgupta, R., Banerjee, G., Hidayetullah, S.M. et al. A comparative analysis of statistical, MCDM and machine learning based modification strategies to reduce subjective errors of DRASTIC models. Environ Earth Sci 83, 211 (2024). https://doi.org/10.1007/s12665-024-11515-3
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DOI: https://doi.org/10.1007/s12665-024-11515-3