Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-022-19818-3.pdf
Reference88 articles.
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2. Abba SI, Pham QB, Saini G, Linh NTT, Ahmed AN, Mohajane M, Khaledian M, Abdulkadir RA, Bach Q (2020b) Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-09689-x
3. Abed BS, Farhan AA-R, Ismail AH, Al Aani S (2021) Water quality index toward a reliable assessment for water supply uses: a novel approach. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-021-03338-7
4. Adimalla N, Qian H (2019) Groundwater quality evaluation using water quality index (WQI) for drinking purposes and human health risk (HHR) assessment in an agricultural region of Nanganur, south India. Ecotox Environ Saf 176:153–161. https://doi.org/10.1016/j.ecoenv.2019.03.066
5. Ahada CPS, Suthar S (2017) Assessment of human health risk associated with high groundwater fluoride intake in southern districts of Punjab. Expo Health, India. https://doi.org/10.1007/s12403-017-0268-4
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