[1] Singha, S., et al.: Prediction of groundwater quality using efficient machine learning techniques. Chemosphere 276(2021), 130265

[2] Ransom, K.M., et al.: Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States. Science of the Total Environment 807, 151065 (2022)

[3] Tran, D.A., et al.: Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecological Indicators 127, 107790 (2021)

[4] Khoi, D.N., et al.: Using machine learning models for predicting the water quality index in the La Buong River, Vietnam. Water 14(10), 1552 (2022)

[5] Khan, M.S.I., et al.: Water quality prediction and classification based on principal component regression and gradient boosting classifier approach. J. King Saud Univ.-Comp. Info. Sci. 34(8), 4773–4781 (2022)

[6] Fu, Z.: Water Quality Prediction Based on Machine Learning Techniques. University of Nevada, Las Vegas, PhD diss. (2020)

[7] Hussein, E.A., Thron, C., Ghaziasgar, M., Bagula, A., Vaccari, M.: Groundwater prediction using machine-learning tools. Algorithms 13(11), 300 (2020)

[8] Zhu, M., et al.: A review of the application of machine learning in water quality evaluation. Eco-Environment & Health (2022)

[9] Jeihouni, M., Toomanian, A., Mansourian, A.: Decision tree-based data mining and rule induction for identifying high-quality groundwater zones to water supply management: a novel hybrid use of data mining and GIS. Water Resour. Manage 34, 139–154 (2020)

[10] Hanoon, M.S., et al.: Application of soft computing in predicting groundwater quality parameters. Frontiers in Environmental Science 12 (2022)

[11] Khalil, A., Almasri, M.N., McKee, M., Kaluarachchi, J.J.: Applicability of statistical learning algorithms in groundwater quality modelling. Water Resources Research 41(5) (2005)

[12] Kopsiaftis, G., Protopapadakis, E., Voulodimos, A., Doulamis, N., Mantoglou, A.: Gaussian process regression tuned by Bayesian optimisation for seawater intrusion prediction. Computational intelligence and neuroscience 2019 (2019)

[13] Lee, K.-J., Yun, S.-T., Soonyoung, Y., Kim, K.-H., Lee, J.-H., Lee, S.-H.: The combined use of self-organising map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea. J. Hydrol. 569, 685–697 (2019)

[14] Agrawal, P., et al.: Exploring artificial intelligence techniques for groundwater quality assessment. Water 13(9), 1172 (2021)

[15] Ransom, K.M., Nolan, B.T., Stackelberg, P.E., Belitz, K., Fram, M.S.: Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States. Science of the Total Environment 807, 151065 (2022)

[16] Yadav, B., Gupta, P.K., Patidar, N., Himanshu, S.K.: Ensemble modelling framework for groundwater level prediction in urban areas of India. Science of the Total Environment 712, 135539 (2020)

[17] Heddam, S.: Generalised regression neural network based approach as a new tool for predicting Total Dissolved Gas (TDG) downstream of spillways of dams: a case study of Columbia river basin dams, USA. Environmental Processes 4(1), 235–253 (2017)

[18] Cho, K.H., Sthiannopkao, S., Pachepsky, YA., Kim, K.-W., Kim, J.H.: Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Water Research 45(17), 5535–5544 (2011)

[19] Mohapatra, J.B., Jha, P., Jha, M.K., Biswal, S.: Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India. Sci. Total Environ. 785, 147319 (2021)

[20] Singha, S., Pasupuleti, S., Singha, S.S., Singh, R., Kumar, S.: Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276, 130265 (2021)

[21] Tran, D.A., et al.: Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecological Indicators 127, 107790 (2021)

[22] Khoi, D.N., Quan, N.T., Linh, D.Q., Thao Nhi, P.T., Diem Thuy, N.T.: Using machine learning models for predicting the water quality index in the La Buong River, Vietnam. Water 14(10), 1552 (2022)

[23] Alasadi, S.A., Bhaya, W.S.: Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12(16), 4102–4107 (2017)

[24] Chernukho, E.V.: Universal algorithm for testing statistical hypotheses with respect to the distribution. J. Eng. Phys. Thermophys. 83, 602–610 (2010)

[25] Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)

[26] Xu, J., et al.: An alternative to laboratory testing: Random forest-based water quality prediction framework for inland and nearshore water bodies. Water 13(22), 3262 (2021)

[27] Kisi, O., Parmar, K.S.: Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology 534,, 104–112 (2016)

[28] Khan, M.S.I., Islam, N., Uddin, J., Islam, S., Nasir, M.K.: Water quality prediction and classification based on principal component regression and gradient boosting classifier approach. J. King Saud Univ.-Comp. Info. Sci. 34(8), 4773–4781 (2022)

[29] Nugraha, B., Nambiar, A., Bauschert, T.: Performance evaluation of botnet detection using deep learning techniques. In: 2020 11th International Conference on Network of the Future (NoF), pp. 141–149. IEEE (2020)

[30] World Health Organization: WHO Guidelines for drinking water quality (2023). Retrieved from: https://cpcb.nic.in/who-guidelines-for-drinking-water-quality/