Assessment of groundwater quality using water quality index, multivariate statistical analysis and machine learning … – Springer

• Abdullahi, A. A., Ighalo, J. O., Ajala, O. J., & Ayika, S. (2020). Physicochemical analysis and heavy metals remediation of pharmaceutical industry effluent using bentonite clay modified by h2so4 and hcl. Journal of the Turkish Chemical Society, Section a: Chemistry, 7(3), 727–744. https://doi.org/10.18596/jotcsa.703913

  Article  CAS  Google Scholar 

• Adimalla, N., & Wu, J. (2019). Groundwater quality and associated health risks in a semi-arid region of south India: Implication to sustainable groundwater management. Human and Ecological Risk Assessment, 25(1–2), 191–216. https://doi.org/10.1080/10807039.2018.1546550

  Article  CAS  Google Scholar 

• Adimalla, N., Li, P., & Qian, H. (2019). Evaluation of groundwater contamination for fluoride and nitrate in semi-arid region of Nirmal Province, South India: A special emphasis on human health risk assessment (HHRA). Human and Ecological Risk Assessment: An International Journal, 25(5), 1107–1124. https://doi.org/10.1080/10807039.2018.1460579

  Article  CAS  Google Scholar 

• Aher, S., Deshmukh, K., Gawali, P., Zolekar, R., & Deshmukh, P. (2022). Hydrogeochemical characteristics and groundwater quality investigation along the basinal cross-section of Pravara River, Maharashtra, India. Journal of Asian Earth Sciences: X, 7, 100082. https://doi.org/10.1016/j.jaesx.2022.100082

  Article  Google Scholar 

• Ahmed, U., Mumtaz, R., Anwar, H., Shah, A. A., Irfan, R., & García-Nieto, J. (2019). Efficient water quality prediction using supervised machine learning. Water (switzerland). https://doi.org/10.3390/w11112210

  Article  Google Scholar 

• Alam, R., Ahmed, Z., Seefat, S. M., & Nahin, K. T. K. (2021). Assessment of surface water quality around a landfill using multivariate statistical method, Sylhet, Bangladesh. Environmental Nanotechnology Monitoring and Management, 15, 100422. https://doi.org/10.1016/j.enmm.2020.100422

  Article  CAS  Google Scholar 

• Alshehri, F., & Rahman, A. (2023). Coupling machine and deep learning with explainable artificial intelligence for improving prediction of groundwater quality and decision-making in arid region. Saudi Arabia Water, 15(12), 2298. https://doi.org/10.3390/w15122298

  Article  Google Scholar 

• APHA. (2017). Standard methods for the examination of water and wastewater. Standard methods for the examination of water and wastewater. Washington: American Public Health Association.

  Google Scholar 

• Asadollah, S. B. H. S., Sharafati, A., Motta, D., & Yaseen, Z. M. (2021). River water quality index prediction and uncertainty analysis: A comparative study of machine learning models. Journal of Environmental Chemical Engineering, 9(1), 104599. https://doi.org/10.1016/j.jece.2020.104599

  Article  CAS  Google Scholar 

• Ayoob, S., & Gupta, A. K. (2006). Fluoride in drinking water: A review on the status and stress effects. Critical Reviews in Environmental Science and Technology. https://doi.org/10.1080/10643380600678112

  Article  Google Scholar 

• Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). a review on the role of machine learning in enabling IoT based healthcare applications. IEEE Access, 9, 38859–38890. https://doi.org/10.1109/ACCESS.2021.3059858

  Article  Google Scholar 

• BIS. (2012). 10500 Indian standard drinking water–specification, second revision. New Delhi: Bureau of Indian Standards.

  Google Scholar 

• Bisht, A., Kamboj, N., & Kamboj, V. (2022). Groundwater quality and potential health risk assessment in the vicinity of solid waste dumping sites of quaternary shallow water aquifers of Ganga Basin. Water, Air and Soil Pollution. https://doi.org/10.1007/s11270-022-05954-6

  Article  Google Scholar 

• Boateng, T. K., Opoku, F., & Akoto, O. (2019). Heavy metal contamination assessment of groundwater quality: A case study of Oti landfill site, Kumasi. Applied Water Science. https://doi.org/10.1007/s13201-019-0915-y

  Article  Google Scholar 

• Bui, D. T., Khosravi, K., Tiefenbacher, J., Nguyen, H., & Kazakis, N. (2020). Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Science of the Total Environment, 721, 137612. https://doi.org/10.1016/j.scitotenv.2020.137612

  Article  CAS  Google Scholar 

• CGWB. (2009). District groundwater brochure Vellore district, Tamil Nadu. Technical report series.

• CPCB. (2007). Guidelines for water quality monitoring central pollution Control board Parivesh Bhawan East Arjun Nagar, Delhi-32

• Dahiya, R. P., & Chandra, A. (2006). Leachate characterization and assessment of groundwater pollution near municipal solid waste landfill site. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-006-1505-7

  Article  Google Scholar 

• Duzan, H., & Shariff, N. S. B. M. (2015). Ridge regression for solving the multicollinearity problem: Review of methods and models. Journal of Applied Sciences, 15(3), 392–404. https://doi.org/10.3923/jas.2015.392.404

  Article  Google Scholar 

• El Fadili, H., Ben Ali, M., El Mahi, M., Cooray, A. T., & Mostapha Lotfi, E. (2022). A comprehensive health risk assessment and groundwater quality for irrigation and drinking purposes around municipal solid waste sanitary landfill: A case study in Morocco. Environmental Nanotechnology, Monitoring and Management, 18, 100698. https://doi.org/10.1016/j.enmm.2022.100698

  Article  CAS  Google Scholar 

• El-Rawy, M., Batelaan, O., Alshehri, F., Almadani, S., Ahmed, M. S., & Elbeltagi, A. (2023). An integrated GIS and machine-learning technique for groundwater quality assessment and prediction in Southern Saudi Arabia. Water, 15(13), 2448. https://doi.org/10.3390/w15132448

  Article  CAS  Google Scholar 

• Fatoba, J. O., Sanuade, O. A., Hammed, O. S., & Igboama, W. W. (2017). The use of multivariate statistical analysis in the assessment of groundwater hydrochemistry in some parts of southwestern Nigeria. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-017-3125-7

  Article  Google Scholar 

• Golkarian, A., Naghibi, S. A., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-018-6507-8

  Article  Google Scholar 

• Hinge, G., Bharali, B., Baruah, A., & Sharma, A. (2022). Integrated groundwater quality analysis using water quality index, GIS and multivariate technique: A case study of Guwahati City. Environmental Earth Sciences, 81(16), 1–15. https://doi.org/10.1007/s12665-022-10544-0

  Article  CAS  Google Scholar 

• Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Applications to nonorthogonal problems. Technometrics, 12(1), 69–82. https://doi.org/10.1080/00401706.1970.10488635

  Article  Google Scholar 

• Horning, N. (2010). Random forests: An algorithm for image classification and generation of continuous fields data sets. In International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2010, pp 1–6

• Hussain, A., Deshwal, A., Priyadarshi, M., Pathak, S., Sambandam, G., Chand, S., & Shukla, A. K. (2024). Landfill leachate analysis from selected landfill sites and its impact on groundwater quality, New Delhi, India. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-04403-6

  Article  Google Scholar 

• Koliyabandara, S. M. P. A., Asitha, T. C., Sudantha, L., & Siriwardana, C. (2020). Assessment of the impact of an open dumpsite on the surface water quality deterioration in Karadiyana, Sri Lanka. Environmental Nanotechnology, Monitoring and Management, 14, 100371. https://doi.org/10.1016/j.enmm.2020.100371

  Article  Google Scholar 

• Kumar, P. J. S., Babu, P. T., & Delson, P. D. (2013). Level and distribution of nitrate in groundwater in parts of Vellore district, Tamil nadu. India Elixir Pollution, 55(3), 12782–12784.

  Google Scholar 

• Kumar, M., Studies, E., Ramanathan, A., & Rao, M. S. (2014). Identification and evaluation of hydrogeochemical processes in the groundwater environment of Delhi, India. Environmental Geology. https://doi.org/10.1007/s00254-006-0275-4

  Article  Google Scholar 

• Kumari, M., & Rai, S. C. (2020). Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes using water quality index in semi arid region of India. Journal of the Geological Society of India, 95(2), 159–168. https://doi.org/10.1007/s12594-020-1405-4

  Article  CAS  Google Scholar 

• Kumari, P., Gupta, N. C., Kaur, A., & Singh, K. (2019). Application of principal component analysis and correlation for assessing groundwater contamination in and around municipal solid waste landfill of Ghazipur, Delhi. Journal of the Geological Society of India, 94(6), 595–604. https://doi.org/10.1007/s12594-019-1366-7

  Article  CAS  Google Scholar 

• Li, P., & Srinivasamoorthy, K. (2021). Sources and consequences of groundwater contamination. Archives of Environmental Contamination and Toxicology, 80, 1–10.

  Article  Google Scholar 

• Li, X., Ding, J., & Ilyas, N. (2021). Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study. Water Science and Technology: Water Supply. https://doi.org/10.2166/ws.2020.381

  Article  Google Scholar 

• Littlechild, J. A., Nardi-Dei, V., Kurihara, T., Park, C., Esaki, N., Soda, K., et al. (2011). Linear ridge regression with spatial constraint for generation of parametric images in dynamic positron emission tomography studies. Current Opinion in Biotechnology, 9(4), 478–499.

  Google Scholar 

• Manzar, M. S., Benaafi, M., Costache, R., Alagha, O., MuazuZubair, N. D. M., et al. (2022). New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia. Ecological Informatics, 70, 101696. https://doi.org/10.1016/j.ecoinf.2022.101696

  Article  Google Scholar 

• Marove, C. A., Sotozono, R., Tangviroon, P., Tabelin, C. B., & Igarashi, T. (2022). Assessment of soil, sediment and water contaminations around open-pit coal mines in Moatize, Tete province, Mozambique. Environmental Advances, 8, 100215. https://doi.org/10.1016/j.envadv.2022.100215

  Article  CAS  Google Scholar 

• Martins dos Santos, J., Amaral, L. M., & Martinho, G. (2023). Effects of landfill age, climate, and size on leachate from urban waste landfills in Portugal: A statistics and machine learning analysis. Waste Management, 172, 192–207. https://doi.org/10.1016/j.wasman.2023.10.027

  Article  CAS  Google Scholar 

• Masood, A., Aslam, M., Pham, Q. B., Khan, W., & Masood, S. (2022). Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality. Environmental Science and Pollution Research, 29(18), 26860–26876. https://doi.org/10.1007/s11356-021-17594-0

  Article  CAS  Google Scholar 

• Matta, G., Kumar, A., Nayak, A., & Kumar, P. (2022). Appraisal of spatial–temporal variation and pollution source estimation of Ganga River system through pollution indices and environmetrics in Upper Ganga basin. Applied Water Science. https://doi.org/10.1007/s13201-021-01552-9

  Article  Google Scholar 

• Mohamed, I., Othman, F., Ibrahim, A. I. N., Alaa-Eldin, M. E., & Yunus, R. M. (2015). Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia. Environmental Monitoring and Assessment, 187(1), 1–12. https://doi.org/10.1007/s10661-014-4182-y

  Article  CAS  Google Scholar 

• Mohammad, A., Singh, D. N., Podlasek, A., Osinski, P., & Koda, E. (2022). Leachate characteristics: Potential indicators for monitoring various phases of municipal solid waste decomposition in a bioreactor landfill. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2022.114683

  Article  Google Scholar 

• Mora, A., Mahlknecht, J., Rosales-lagarde, L., & Hernández-antonio, A. (2017). Assessment of major ions and trace elements in groundwater supplied to the Monterrey metropolitan area, Nuevo León, Mexico. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-017-6096-y

  Article  Google Scholar 

• Mukherjee, I., & Kumar, U. (2018). Groundwater fluoride contamination, probable release, and containment mechanisms: A review on Indian context. Environmental Geochemistry and Health. https://doi.org/10.1007/s10653-018-0096-x

  Article  Google Scholar 

• Ok, A. O., Akar, O., & Gungor, O. (2012). Evaluation of random forest method for agricultural crop classification. European Journal of Remote Sensing, 45(1), 421–432. https://doi.org/10.5721/EuJRS20124535

  Article  Google Scholar 

• Osei-Bryson, K. M. (2004). Evaluation of decision trees: A multi-criteria approach. Computers and Operations Research, 31(11), 1933–1945. https://doi.org/10.1016/S0305-0548(03)00156-4

  Article  Google Scholar 

• Ossenbruggen, P. J., Collins, M. R., & Gaudard, M. A. (1988). System model development with ill-conditioned data: Case studies of trihalomethane formation in drinking water. Civil Engineering Systems, 5(1), 31–41. https://doi.org/10.1080/02630258808970500

  Article  Google Scholar 

• Outroy, S. R., Arichandan, R. H., Ohanty, J. K. M., & Anda, C. R. P. (2013). A statistical appraisal to hydrogeochemistry of fluoride contaminated ground water in Nayagarh district. Odisha, 81, 350–360.

  Google Scholar 

• Praisegod, C., Theophilus, I., & Jarvis, P. (2018). Ecotoxicology and environmental safety fluoride contamination in groundwater sources in Southwestern Nigeria: Assessment using multivariate statistical approach and human health risk. Ecotoxicology and Environmental Safety, 156, 391–402. https://doi.org/10.1016/j.ecoenv.2018.03.022

  Article  CAS  Google Scholar 

• Rezaei, A., Hassani, H., Fard Mousavi, S. B., Hassani, S., & Jabbari, N. (2019a). Assessment of heavy metals contamination in surface soils in meiduk copper mine area, SE Iran. Earth Science Malaysia, 3(2), 01–08. https://doi.org/10.26480/esmy.02.2019.01.08

  Article  Google Scholar 

• Rezaei, A., Hassani, H., Hassani, S., Jabbari, N., Fard Mousavi, S. B., & Rezaei, S. (2019b). Evaluation of groundwater quality and heavy metal pollution indices in Bazman basin, southeastern Iran. Groundwater for Sustainable Development. https://doi.org/10.1016/j.gsd.2019.100245

  Article  Google Scholar 

• Rezaei, A., Hassani, H., & Jabbari, N. (2019c). Evaluation of groundwater quality and assessment of pollution indices for heavy metals in North of Isfahan Province, Iran. Sustainable Water Resources Management, 5(2), 491–512. https://doi.org/10.1007/s40899-017-0209-1

  Article  Google Scholar 

• Rong, L., Zhang, C., Jin, D., & Dai, Z. (2017). Assessment of the potential utilization of municipal solid waste from a closed irregular landfill. Journal of Cleaner Production, 142, 413–419. https://doi.org/10.1016/j.jclepro.2015.10.050

  Article  CAS  Google Scholar 

• Sajil Kumar, P. J., Davis Delson, P., & Thomas Babu, P. (2012). Appraisal of heavy metals in groundwater in Chennai city using a HPI model. Bulletin of Environmental Contamination and Toxicology, 89(4), 793–798. https://doi.org/10.1007/s00128-012-0794-5

  Article  CAS  Google Scholar 

• Sajil Kumar, P. J., Davis Delson, P., Vernon, J. G., & James, E. J. (2013). A linear regression model (LRM) for groundwater chemistry in and around the Vaniyambadi industrial area, Tamil Nadu, India. Chinese Journal of Geochemistry, 32(1), 19–26. https://doi.org/10.1007/s11631-013-0602-x

  Article  CAS  Google Scholar 

• Sajil Kumar, P. J., Jegathambal, P., & James, E. J. (2014). Factors influencing the high fluoride concentration in groundwater of Vellore District, South India. Environmental Earth Sciences, 72(7), 2437–2446. https://doi.org/10.1007/s12665-014-3152-6

  Article  CAS  Google Scholar 

• Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science. https://doi.org/10.1007/s42979-021-00592-x

  Article  Google Scholar 

• Sharma, P., & Kumar, S. (2021). Characterization and phytotoxicity assessment of organic pollutants in old and fresh municipal solid wastes at open dump site: A case study. Environmental Technology and Innovation. https://doi.org/10.1016/j.eti.2021.101938

  Article  Google Scholar 

• Siddiqi, S. A., Al-Mamun, A., Sana, A., Baawain, M. S., & Choudhury, M. R. (2022). Characterization and pollution potential of leachate from urban landfills during dry and wet periods in arid regions. Water Supply, 22(3), 3462–3483. https://doi.org/10.2166/WS.2021.392

  Article  CAS  Google Scholar 

• Sihag, P., Mohsenzadeh Karimi, S., & Angelaki, A. (2019). Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Applied Water Science, 9(5), 1–9. https://doi.org/10.1007/s13201-019-1007-8

  Article  Google Scholar 

• Sihag, P., Angelaki, A., & Chaplot, B. (2020). Estimation of the recharging rate of groundwater using random forest technique. Applied Water Science, 10(7), 1–11. https://doi.org/10.1007/s13201-020-01267-3

  Article  Google Scholar 

• Singh, A., & Chandel, M. K. (2020). Effect of ageing on waste characteristics excavated from an Indian dumpsite and its potential valorisation. Process Safety and Environmental Protection, 134, 24–35. https://doi.org/10.1016/j.psep.2019.11.025

  Article  CAS  Google Scholar 

• Sreedevi, P. D., Sreekanth, P. D., Ahmed, S., & Reddy, D. V. (2018). Appraisal of groundwater quality in a crystalline aquifer: A chemometric approach. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-018-3480-z

  Article  Google Scholar 

• Swain, S., Sahoo, S., & Taloor, A. K. (2022). Groundwater quality assessment using geospatial and statistical approaches over Faridabad and Gurgaon districts of National Capital Region, India. Applied Water Science. https://doi.org/10.1007/s13201-022-01604-8

  Article  Google Scholar 

• Tyagi, S., Sharma, B., Singh, P., & Dobhal, R. (2020). Water quality assessment in terms of water quality index. American Journal of Water Resources, 1(3), 34–38.

  Article  Google Scholar 

• Uddin, M. G., Nash, S., & Olbert, A. I. (2021). A review of water quality index models and their use for assessing surface water quality. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2020.107218

  Article  Google Scholar 

• Uddin, Md. G., Olbert, A. I., Nash, S., & Rahman, A. (2022). A comprehensive method for improvement of water quality index ( WQI ) models for coastal water quality assessment. Water Research, 219, 118532. https://doi.org/10.1016/j.watres.2022.118532

  Article  CAS  Google Scholar 

• Umana, I. M., Neji, P. A., & Agwupuye, J. A. (2022). Assessment of underground water quality in Okobo local government area of Akwa Ibom State, Nigeria. Applied Water Science, 12(5), 1–12. https://doi.org/10.1007/s13201-022-01614-6

  Article  CAS  Google Scholar 

• Usman, U. N., Toriman, M. E., & Juahir, H. (2015). Assessment of groundwater quality using multivariate statistical techniques in Terengganu. Science and Technology. https://doi.org/10.5923/j.scit.20140403.02

  Article  Google Scholar 

• WHO. (2015). Waste and human health. Who, 35, 5441.

  Google Scholar 

• WHO. (2022). Guidelines for drinking-water quality: Fourth edition incorporating the first and second addenda (Vol. 33). Geneva: World Health Organization.

  Google Scholar 

• Xiaohong, D., Huajiang, C., Bagherzadeh, S. A., Shayan, M., & Akbari, M. (2020). Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method. Physica a: Statistical Mechanics and Its Applications, 537, 122782. https://doi.org/10.1016/j.physa.2019.122782

  Article  CAS  Google Scholar 

• Xu, M., Watanachaturaporn, P., Varshney, P. K., & Arora, M. K. (2005). Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322–336. https://doi.org/10.1016/j.rse.2005.05.008

  Article  Google Scholar 

Download references