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contamination in Hawaii, USA. J Conta Hydrol 153:1–23
Mazumder GDN (2008) Chronic arsenic toxicity & human health. Ind J Med Res 128(4):436–447
Messier KP, Wheeler DC, Flory AR, Jones RR, Patel D, Nolan BT, Ward MH (2019) Modeling
groundwater nitrate exposure in private wells of North Carolina for the agricultural health study.
Sci Total Environ 655:512–519
Miraki S, Zanganeh SH, Chapi K, Singh VP, Shirzadi A, Shahabi H, Pham BT (2019) Mapping
groundwater potential using a novel hybrid intelligence approach. Water Resour Manag 33
(1):281–302
Mitra A, Chatterjee S, Gupta DK (2020) Environmental arsenic exposure and human health risk. In:
Fares A, Singh SK (eds) Arsenic water resources contamination: challenges and solutions.
Springer, Cham, pp 103–129
Muchlinski D, David S, Jingrui H, Matthew K (2016) Comparing random forest with logistic
regression for predicting class-imbalanced civil war onset data. Polit Anal 24(1):87–103
Murcott S (2012) Arsenic contamination in the world. An international sourcebook
Nadiri AA, Fijani E, Tsai FTC, Moghaddam AA (2013) Supervised committee machine with
artificial intelligence for prediction of fluoride concentration. J Hydroinf 15(4):1474–1490
Nayak B, Roy MM, Das B, Pal A, Sengupta MK, De SP, Chakraborti D (2009) Health effects of
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the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405
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(1):59–79
Park Y, Ligaray M, Kim YM, Kim JH, Cho KH, Sthiannopkao S (2016) Development of enhanced
groundwater arsenic prediction model using machine learning approaches in Southeast Asian
countries. Desalinat Water Treat 57(26):12227–12236
Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Tran VP, Khoi DK, Shirzadi A
(2018) A novel hybrid approach of landslide susceptibility modeling using rotation forest
ensemble and different base classifiers. Geocarto Int:1–38
Pham BT, Abolfazl J, Prakash I, Singh SK, Quoc NK, Bui DT (2019) Hybrid computational
intelligence models for groundwater potential mapping. Catena 182:101–104
Phong TV, Phan TT, Prakash I, Singh SK, Shirzadi A, Chapi K, Ly HB, Ho LS, Quoc NK, Pham
BT (2019) Landslide susceptibility modeling using different artificial intelligence methods: a
case study at Muong Lay district, Vietnam. Geocarto Int:1–24
Podgorski JE, Eqani SAMAS, Khanam T, Ullah R, Shen H, Berg M (2017) Extensive arsenic
contamination in high-pH unconfined aquifers in the Indus Valley. Sci Adv 3(8):e1700935
Podgorski JE, Labhasetwar P, Saha D, Berg M (2018) Prediction modeling and mapping of
groundwater fluoride contamination throughout India. Environ Sci Technol 52(17):9889–9898
Ritchie H, Roser M (2019) Clean water. Our world in data
4 Application of Artificial Intelligence in Predicting Groundwater Contaminants
103
from spatial data using machine learning. Sci Total Environ 668:1317–1327
Lado LR, Polya D, Winkel L, Berg M, Hegan A (2008) Modelling arsenic hazard in Cambodia: a
geostatistical approach using ancillary data. Appl Geochem 23(11):3010–3018
Mair A, El-Kadi AI (2013) Logistic regression modeling to assess groundwater vulnerability to
contamination in Hawaii, USA. J Conta Hydrol 153:1–23
Mazumder GDN (2008) Chronic arsenic toxicity & human health. Ind J Med Res 128(4):436–447
Messier KP, Wheeler DC, Flory AR, Jones RR, Patel D, Nolan BT, Ward MH (2019) Modeling
groundwater nitrate exposure in private wells of North Carolina for the agricultural health study.
Sci Total Environ 655:512–519
Miraki S, Zanganeh SH, Chapi K, Singh VP, Shirzadi A, Shahabi H, Pham BT (2019) Mapping
groundwater potential using a novel hybrid intelligence approach. Water Resour Manag 33
(1):281–302
Mitra A, Chatterjee S, Gupta DK (2020) Environmental arsenic exposure and human health risk. In:
Fares A, Singh SK (eds) Arsenic water resources contamination: challenges and solutions.
Springer, Cham, pp 103–129
Muchlinski D, David S, Jingrui H, Matthew K (2016) Comparing random forest with logistic
regression for predicting class-imbalanced civil war onset data. Polit Anal 24(1):87–103
Murcott S (2012) Arsenic contamination in the world. An international sourcebook
Nadiri AA, Fijani E, Tsai FTC, Moghaddam AA (2013) Supervised committee machine with
artificial intelligence for prediction of fluoride concentration. J Hydroinf 15(4):1474–1490
Nayak B, Roy MM, Das B, Pal A, Sengupta MK, De SP, Chakraborti D (2009) Health effects of
groundwater fluoride contamination. Clinical Toxicol 47(4):292–295
NGWA (2020) Groundwater facts. https://www.ngwa.org/what-is-groundwater/About-groundwa
ter/groundwater-facts. Accessed 4 Feb 2020
Nordstrom DK (2002) Worldwide occurrences of arsenic in ground water. Ame Assoc Advance Sci
296:2143
Oremland RS, Stolz JF (2003) The ecology of arsenic. Science 300(5621):939–944
Ouedraogo I, Defourny P, Vanclooster M (2019) Application of random forest regression and
comparison of its performance to multiple linear regression in modeling groundwater nitrate
concentration at the African continent scale. Hydrogeol J 27(3):1081–1098
Ozdemir A (2011) Using a binary logistic regression method and GIS for evaluating and mapping
the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405
(1–2):123–136
Ozsvath DL (2009) Fluoride and environmental health: a review. Rev Environ Sci Biotechnol 8
(1):59–79
Park Y, Ligaray M, Kim YM, Kim JH, Cho KH, Sthiannopkao S (2016) Development of enhanced
groundwater arsenic prediction model using machine learning approaches in Southeast Asian
countries. Desalinat Water Treat 57(26):12227–12236
Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Tran VP, Khoi DK, Shirzadi A
(2018) A novel hybrid approach of landslide susceptibility modeling using rotation forest
ensemble and different base classifiers. Geocarto Int:1–38
Pham BT, Abolfazl J, Prakash I, Singh SK, Quoc NK, Bui DT (2019) Hybrid computational
intelligence models for groundwater potential mapping. Catena 182:101–104
Phong TV, Phan TT, Prakash I, Singh SK, Shirzadi A, Chapi K, Ly HB, Ho LS, Quoc NK, Pham
BT (2019) Landslide susceptibility modeling using different artificial intelligence methods: a
case study at Muong Lay district, Vietnam. Geocarto Int:1–24
Podgorski JE, Eqani SAMAS, Khanam T, Ullah R, Shen H, Berg M (2017) Extensive arsenic
contamination in high-pH unconfined aquifers in the Indus Valley. Sci Adv 3(8):e1700935
Podgorski JE, Labhasetwar P, Saha D, Berg M (2018) Prediction modeling and mapping of
groundwater fluoride contamination throughout India. Environ Sci Technol 52(17):9889–9898
Ritchie H, Roser M (2019) Clean water. Our world in data
4 Application of Artificial Intelligence in Predicting Groundwater Contaminants
103