Rizeei HM, Azeez OS, Pradhan B, Khamees HH (2018) Assessment of groundwater nitrate
contamination hazard in a semi-arid region by using integrated parametric IPNOA and datadriven logistic regression models. Environ Monit Assess 190(11):633
Rizeei HM, Pradhan B, Saharkhiz MA, Lee S (2019) Groundwater aquifer potential modeling using
an ensemble multi-adoptive boosting logistic regression technique. J Hydrol 579:124–172
Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive modeling of groundwater nitrate pollution using random Forest and multisource variables
related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern
Spain). Sci Total Environ 476:189–206
Rodriguez-Galiano VF, Luque-Espinar JA, Chica-Olmo M, Mendes MP (2018) Feature selection
approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters,
embedded and wrapper methods. Sci Total Environ 624:661–672
Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B (2018) A
novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci Total Environ 644:954–962
Sameen MH, Pradhan B, Lee S (2019) Self-learning random forests model for mapping groundwater yield in data-scarce areas. Natu Resour Res 28(3):757–775
Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring
Bayesian optimization for landslide susceptibility assessment. Catena 186:104249
Schwarzenbach RP, Thomas E, Thomas BH, Gunten UV, Wehrli B (2010) Global water pollution
and human health. Ann Rev Environ Resour 35:109–136
Schweitzer L, Noblet J (2018) Water contamination and pollution. In: Green chemistry. Elsevier, pp
261–290
Şen Z (2014) Practical and applied hydrogeology. Elsevier
Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Ahmad BB, Bui
DT (2019) Uncertainties of prediction accuracy in shallow landslide modeling: sample size and
raster resolution. Catena 178:172–188
Shmueli G (2010) To explain or to predict? Stat Sci 25(3):289–310
Singh SK (2017) Conceptual framework of a cloud-based decision support system for arsenic health
risk assessment. Environ Sys Decis 37(4):435–450. https://doi.org/10.1007/s10669-017-9641-x
Singh, SK (2018) Application of artificial intelligence in environmental modelling and sustainability. In Euro Scicon Conference on Applied Science, Biofuels & Petroleum Engineering, Athens,
Greece, November 12–13, 2018
Singh SK, Srivastava PK, Pandey AC (2013) Fluoride contamination mapping of groundwater in
Northern India integrated with geochemical indicators and GIS. Water Sci Technol Water Supp
13(6):1513–1523
Singh SK, Brachfeld SA, Taylor RW (2016) Evaluating hydrogeological and topographic controls
on groundwater arsenic contamination in the Middle-Ganga plain in India: towards developing
sustainable arsenic mitigation models. In: Emerging issues in groundwater resources. Springer,
pp 263–287
Singh SK, Taylor RW, Rahman MM, Pradhan B (2018) Developing robust arsenic awareness
prediction models using machine learning algorithms. J Environ Manag 211:125–137. https://
doi.org/10.1016/j.jenvman.2018.01.044
Sinha D, Prasad P (2020) Health effects inflicted by chronic low-level arsenic contamination in
groundwater: a global public health challenge. J Appl Toxicol 40(1):87–131
Sousa MR, Rudolph DL, Frind E (2014) Threats to groundwater resources in urbanizing watersheds: the Waterloo Moraine and beyond. Can Water Resour J 39(2):193–208
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a
classification and regression tool for compound classification and QSAR modeling. J Informat
Comp Sci 43(6):1947–1958
Tchounwou PB, Yedjou CG, Udensi UK, Pacurari M, Stevens JJ, Patlolla AK, Noubissi F, Kumar S
(2019) State of the science review of the health effects of inorganic arsenic: perspectives for
future research. Environ Toxicol 34(2):188–202
104
S. K. Singh et al.
contamination hazard in a semi-arid region by using integrated parametric IPNOA and datadriven logistic regression models. Environ Monit Assess 190(11):633
Rizeei HM, Pradhan B, Saharkhiz MA, Lee S (2019) Groundwater aquifer potential modeling using
an ensemble multi-adoptive boosting logistic regression technique. J Hydrol 579:124–172
Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive modeling of groundwater nitrate pollution using random Forest and multisource variables
related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern
Spain). Sci Total Environ 476:189–206
Rodriguez-Galiano VF, Luque-Espinar JA, Chica-Olmo M, Mendes MP (2018) Feature selection
approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters,
embedded and wrapper methods. Sci Total Environ 624:661–672
Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B (2018) A
novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci Total Environ 644:954–962
Sameen MH, Pradhan B, Lee S (2019) Self-learning random forests model for mapping groundwater yield in data-scarce areas. Natu Resour Res 28(3):757–775
Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring
Bayesian optimization for landslide susceptibility assessment. Catena 186:104249
Schwarzenbach RP, Thomas E, Thomas BH, Gunten UV, Wehrli B (2010) Global water pollution
and human health. Ann Rev Environ Resour 35:109–136
Schweitzer L, Noblet J (2018) Water contamination and pollution. In: Green chemistry. Elsevier, pp
261–290
Şen Z (2014) Practical and applied hydrogeology. Elsevier
Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Ahmad BB, Bui
DT (2019) Uncertainties of prediction accuracy in shallow landslide modeling: sample size and
raster resolution. Catena 178:172–188
Shmueli G (2010) To explain or to predict? Stat Sci 25(3):289–310
Singh SK (2017) Conceptual framework of a cloud-based decision support system for arsenic health
risk assessment. Environ Sys Decis 37(4):435–450. https://doi.org/10.1007/s10669-017-9641-x
Singh, SK (2018) Application of artificial intelligence in environmental modelling and sustainability. In Euro Scicon Conference on Applied Science, Biofuels & Petroleum Engineering, Athens,
Greece, November 12–13, 2018
Singh SK, Srivastava PK, Pandey AC (2013) Fluoride contamination mapping of groundwater in
Northern India integrated with geochemical indicators and GIS. Water Sci Technol Water Supp
13(6):1513–1523
Singh SK, Brachfeld SA, Taylor RW (2016) Evaluating hydrogeological and topographic controls
on groundwater arsenic contamination in the Middle-Ganga plain in India: towards developing
sustainable arsenic mitigation models. In: Emerging issues in groundwater resources. Springer,
pp 263–287
Singh SK, Taylor RW, Rahman MM, Pradhan B (2018) Developing robust arsenic awareness
prediction models using machine learning algorithms. J Environ Manag 211:125–137. https://
doi.org/10.1016/j.jenvman.2018.01.044
Sinha D, Prasad P (2020) Health effects inflicted by chronic low-level arsenic contamination in
groundwater: a global public health challenge. J Appl Toxicol 40(1):87–131
Sousa MR, Rudolph DL, Frind E (2014) Threats to groundwater resources in urbanizing watersheds: the Waterloo Moraine and beyond. Can Water Resour J 39(2):193–208
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a
classification and regression tool for compound classification and QSAR modeling. J Informat
Comp Sci 43(6):1947–1958
Tchounwou PB, Yedjou CG, Udensi UK, Pacurari M, Stevens JJ, Patlolla AK, Noubissi F, Kumar S
(2019) State of the science review of the health effects of inorganic arsenic: perspectives for
future research. Environ Toxicol 34(2):188–202
104
S. K. Singh et al.