develop such prediction models. However, there still exists a gap in the selection of
appropriate algorithms. It is also apparent that many such prediction models were
developed from miniscule sample sizes. Larger sample sizes will be required in the
development of robust prediction models. Studies also suggest that since environmental data are highly nonlinearly correlated and multidimensional, it would be
advantageous to explore nonlinear classifiers and hybrid machine learning models.
Accurate and robust prediction models would help in predicting potentially contaminated areas, obviating costly expenditures on laboratory analytical water testing.
Furthermore, environmental managers, policymakers, and local administrations
could use these models to develop proactive mitigation strategies. These models
will empower us to save millions of human lives by preventing their exposure to
these contaminants, especially arsenic and nitrate, in the first place.
References
Alcamo J, Martina F, Michael M (2007) Future long-term changes in global water resources driven
by socio-economic and climatic changes. Hydrolog Sci J 52(2):247–275
Ali S, Thakur SK, Sarkar A, Shekhar S (2016) Worldwide contamination of water by fluoride.
Environ Chem Letters 14(3):291–315
Amini M, Abbaspour KC, Berg M, Winkel L, Hug SJ, Hoehn E, Yang H, Johnson CA (2008a)
Statistical modeling of global geogenic arsenic contamination in groundwater. Environ Sci
Technol 42(10):3669–3675
Amini M, Mueller K, Abbaspour KC, Rosenberg T, Afyuni M, Møller KN, Sarr M, Johnson CA
(2008b) Statistical modeling of global geogenic fluoride contamination in groundwaters. Environ Sci Technol 42(10):3662–3668
Ayotte JD, Nolan BT, Nuckols JR, Cantor KP, Robinson GR, Baris D, Hayes L, Karagas M,
Bress W, Silverman DT (2006) Modeling the probability of arsenic in groundwater in New
England as a tool for exposure assessment. Environ Sci Technol 40(11):3578–3585
Ayotte JD, Nolan BD, Gronberg JO (2016) Predicting arsenic in drinking water wells of the Central
Valley, California. Environ Sci Technol 50(14):7555–7563
Barzegar R, Moghaddam AA, Adamowski J, Fijani E (2017) Comparison of machine learning
models for predicting fluoride contamination in groundwater. Stochastic Environ Res Risk
Assess 31(10):2705–2718
Bhattacharya P, Samal AC, Majumdar J, Santra SC (2010) Arsenic contamination in rice, wheat,
pulses, and vegetables: a study in an arsenic affected area of West Bengal, India. Water Air Soil
Pollut 213(1–4):3–13
Braman RS (1975) Arsenic in the environment. ACS Publications
Bretzler A, Lalanne F, Nikiema J, Podgorski J, Pfenninger N, Berg M, Schirmer M (2017)
Groundwater arsenic contamination in Burkina Faso, West Africa: predicting and verifying
regions at risk. Sci Total Environ 584:958–970
Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Khosravi K, Chen W, Panahi S, Li S
(2018) Novel hybrid evolutionary algorithms for spatial prediction of floods. Sci Rep 8(1):1–14
Bundy LG, Knobeloch L, Webendorfer B, Jackson GW, Shaw BH (1994) Nitrate in Wisconsin
groundwater: sources and concerns. University of Wisconsin–Extension
Burri NM, Weatherl R, Moeck C, Schirmer (2019) A review of threats to groundwater quality in the
anthropocene. Sci Total Environ 684:136–154
4 Application of Artificial Intelligence in Predicting Groundwater Contaminants
101
appropriate algorithms. It is also apparent that many such prediction models were
developed from miniscule sample sizes. Larger sample sizes will be required in the
development of robust prediction models. Studies also suggest that since environmental data are highly nonlinearly correlated and multidimensional, it would be
advantageous to explore nonlinear classifiers and hybrid machine learning models.
Accurate and robust prediction models would help in predicting potentially contaminated areas, obviating costly expenditures on laboratory analytical water testing.
Furthermore, environmental managers, policymakers, and local administrations
could use these models to develop proactive mitigation strategies. These models
will empower us to save millions of human lives by preventing their exposure to
these contaminants, especially arsenic and nitrate, in the first place.
References
Alcamo J, Martina F, Michael M (2007) Future long-term changes in global water resources driven
by socio-economic and climatic changes. Hydrolog Sci J 52(2):247–275
Ali S, Thakur SK, Sarkar A, Shekhar S (2016) Worldwide contamination of water by fluoride.
Environ Chem Letters 14(3):291–315
Amini M, Abbaspour KC, Berg M, Winkel L, Hug SJ, Hoehn E, Yang H, Johnson CA (2008a)
Statistical modeling of global geogenic arsenic contamination in groundwater. Environ Sci
Technol 42(10):3669–3675
Amini M, Mueller K, Abbaspour KC, Rosenberg T, Afyuni M, Møller KN, Sarr M, Johnson CA
(2008b) Statistical modeling of global geogenic fluoride contamination in groundwaters. Environ Sci Technol 42(10):3662–3668
Ayotte JD, Nolan BT, Nuckols JR, Cantor KP, Robinson GR, Baris D, Hayes L, Karagas M,
Bress W, Silverman DT (2006) Modeling the probability of arsenic in groundwater in New
England as a tool for exposure assessment. Environ Sci Technol 40(11):3578–3585
Ayotte JD, Nolan BD, Gronberg JO (2016) Predicting arsenic in drinking water wells of the Central
Valley, California. Environ Sci Technol 50(14):7555–7563
Barzegar R, Moghaddam AA, Adamowski J, Fijani E (2017) Comparison of machine learning
models for predicting fluoride contamination in groundwater. Stochastic Environ Res Risk
Assess 31(10):2705–2718
Bhattacharya P, Samal AC, Majumdar J, Santra SC (2010) Arsenic contamination in rice, wheat,
pulses, and vegetables: a study in an arsenic affected area of West Bengal, India. Water Air Soil
Pollut 213(1–4):3–13
Braman RS (1975) Arsenic in the environment. ACS Publications
Bretzler A, Lalanne F, Nikiema J, Podgorski J, Pfenninger N, Berg M, Schirmer M (2017)
Groundwater arsenic contamination in Burkina Faso, West Africa: predicting and verifying
regions at risk. Sci Total Environ 584:958–970
Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Khosravi K, Chen W, Panahi S, Li S
(2018) Novel hybrid evolutionary algorithms for spatial prediction of floods. Sci Rep 8(1):1–14
Bundy LG, Knobeloch L, Webendorfer B, Jackson GW, Shaw BH (1994) Nitrate in Wisconsin
groundwater: sources and concerns. University of Wisconsin–Extension
Burri NM, Weatherl R, Moeck C, Schirmer (2019) A review of threats to groundwater quality in the
anthropocene. Sci Total Environ 684:136–154
4 Application of Artificial Intelligence in Predicting Groundwater Contaminants
101