A comparative analysis of statistical, MCDM and machine learning based modification strategies to reduce subjective errors of DRASTIC models-Reference-Cited by-同舟云学术

A comparative analysis of statistical, MCDM and machine learning based modification strategies to reduce subjective errors of DRASTIC models

Published:2024-03-25 Issue:7 Volume:83 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Dasgupta Rijurekha^ORCID,Banerjee Gourab^ORCID,Hidayetullah Sekh Mohammad^ORCID,Saha Nilanjan^ORCID,Das Subhasish^ORCID,Mazumdar Asis^ORCID

Funder

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12665-024-11515-3.pdf

Reference52 articles.

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2. Aller L, Thornhill J (1987) DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. In: EPA/600/285/018, Environ Res Lab. US Environ Prot Agency, Ada

3. Antonakos AK, Lambrakis NJ (2007) Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece. J Hydrol 333(2–4):288–304. https://doi.org/10.1016/j.jhydrol.2006.08.014

4. Asghari Moghaddam A, Nouri Sangarab S, KadkhodaieIlkhchi A (2023) Assessing groundwater vulnerability potential using modified DRASTIC in Ajabshir Plain, NW of Iran. Environ Monit Assess 195(4):497. https://doi.org/10.1007/s10661-023-10992-6

5. Babiker IS, Mohamed MA, Hiyama T, Kato K (2005) A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Sci Total Environ 345(1–3):127–140. https://doi.org/10.1016/j.scitotenv.2004.11.005

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