Quevauviller, P. General Introduction: The Need to Protect Groundwater. In: Quevauviller P, editor. Groundwater Science and Policy: An International Overview [Internet]. The Royal Society of Chemistry; p. 0. (2007). Available from: https://doi.org/10.1039/9781847558039-00001
Kløve, B. et al. Groundwater dependent ecosystems. Part I: hydroecological status and trends. Environ. Sci. Policy. 14 (7), 770–781 (2011). https://www.sciencedirect.com/science/article/pii/S1462901111000517
Siebert, S. et al. Groundwater use for irrigation – A global inventory. Hydrol. Earth Syst. Sci. 14 (10), 1863–1880 (2010).
Lanjwani, M. F., Khuhawar, M. Y. & Jahangir Khuhawar, T. M. Assessment of groundwater quality for drinking and irrigation uses in taluka Ratodero, district Larkana, Sindh, Pakistan. Int. J. Environ. Anal. Chem. 102 (16), 4134–4157 (2022).
Neckel, A. et al. Hazardous elements in the soil of urban cemeteries; constructive solutions aimed at sustainability. Chemosphere 262, 128248 (2021).
Ayejoto, D. A., Agbasi, J. C., Egbueri, J. C. & Echefu, K. I. Assessment of oral and dermal health risk exposures associated with contaminated water resources: an update in Ojoto area, Southeast Nigeria. Int. J. Environ. Anal. Chem. 104 (3), 641–661 (2024).
Agbasi, J. C. et al. Water pollution indexing and health risk assessment due to PTE ingestion and dermal absorption for nine human populations in Southeast Nigeria. Groundw. Sustain. Dev. 21, 100921 (2023).
Ukah, B. U., Egbueri, J. C., Unigwe, C. O. & Ubido, O. E. Extent of heavy metals pollution and health risk assessment of groundwater in a densely populated industrial area, Lagos, Nigeria. Int. J. Energy Water Resour. 3 (4), 291–303 (2019).
Egbueri, J. C. Heavy metals pollution source identification and probabilistic health risk assessment of shallow groundwater in Onitsha. Nigeria Anal. Lett. 53 (10), 1620–1638 (2020).
Ubuoh, E. A., Nwogu, F. U., Ofoegbu, C. C. & Chikezie, P. C. Environmental pollution loads on surface water chemistry and potentially ecological risks of inland aquatic ecosystem in South-Eastern State, Nigeria. Environ. Syst. Res. 12 (1), 22 (2023).
Wang, F. & Tessier, A. Zero-valent sulfur and metal speciation in sediment porewaters of freshwater lakes. Environ. Sci. Technol. 43 (19), 7252–7257 (2009).
Wildi, W. et al. River, reservoir and lake sediment contamination by heavy metals downstream from urban areas of Switzerland. Lakes Reserv. Res. Manag. 9 (1), 75–87 (2004).
Jibrin, A. M. et al. Machine learning predictive insight of water pollution and groundwater quality in the Eastern Province of Saudi Arabia. Sci. Rep. 14 (1), 1–17. https://doi.org/10.1038/s41598-024-70610-4 (2024).
Abba, S. I. et al. Fluoride and nitrate enrichment in coastal aquifers of the Eastern Province, Saudi arabia: the influencing factors, toxicity, and human health risks. Chemosphere 336, 139083. https://doi.org/10.1016/j.chemosphere.2023.139083 (2023).
Nourani, V. et al. Spatiotemporal assessment of groundwater quality and quantity using Geostatistical and ensemble artificial intelligence tools. J. Environ. Manage. 355, 120495 (2024).
Tyagi, S. & Sarma, K. Tracing the land use specific impacts on groundwater quality: a chemometric, information entropy WQI and health risk assessment study. Environ. Sci. Pollut Res. 31 (21), 30519–30542 (2024).
Zhang, X., Chen, S., Luo, J., Yan, W. & Shao, H. Development of performance assessment and fault identification strategy based on kernel GDA. In: 2012 Third International Conference on Intelligent Control and Information Processing. 140–145. (2012).
Shin, T. The application of various nonlinear models to describe academic growth trajectories an empirical analysis using four-wave longitudinal achievement data from a large urban school district. Asia Pac. Educ. Rev. 13 (1), 65–76. https://doi.org/10.1007/s12564-011-9168-7 (2012).
Haggerty, R., Sun, J., Yu, H. & Li, Y. Application of machine learning in groundwater quality modeling – A comprehensive review. Water Res. 233, 119745. https://www.sciencedirect.com/science/article/pii/S004313542300180X (2023).
Oh, J. et al. A supervised machine learning approach to discriminate the effect of carcass leachate on shallow groundwater quality around on-farm livestock mortality burial sites. J. Hazard. Mater. 457, 131712 (2023).
Jibrin, A. M., Al-Suwaiyan, M., Yaseen, Z. M. & Abba, S. I. New perspective on density-based Spatial clustering of applications with noise for groundwater assessment. J. Hydrol. 661 (PA), 133566. https://doi.org/10.1016/j.jhydrol.2025.133566 (2025).
Abba, S. I. et al. Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: insight from machine learning and Spatial mapping. Process. Saf. Environ. Prot. 184, 1143–1157. https://doi.org/10.1016/j.psep.2024.02.041 (2024).
Maiti, S., Gupta, S. & Gupta, P. K. Prediction of groundwater quality index and identification of key variables using bayesian neural network. Water Air Soil. Pollut. 235 (10), 664. https://doi.org/10.1007/s11270-024-07459-w (2024).
Gupta, S. & Maiti, S. Comparison between self-organizing map and principal component analysis for water quality assessment and hydro-geochemical characterization in Dyke intruded complex geological settings. Water Environ. J. 37 (3), 512–526. https://doi.org/10.1111/wej.12855 (2023).
Gupta, P. K. & Maiti, S. Enhancing data-driven modeling of fluoride concentration using new data mining algorithms. Environ. Earth Sci. 81 (3), 89. https://doi.org/10.1007/s12665-022-10216-z (2022).
Gupta, S. & Maiti, S. Groundwater quality and fluoride contamination risks in Jharkhand state: A regional IWQI analysis and hydrochemistry insights. J. Environ. Chem. Eng. 13 (5), 118253 (2025). https://www.sciencedirect.com/science/article/pii/S2213343725029495
Usman, J. et al. Evidential neural network and metaheuristic optimization algorithms for sustainable biomass utilization in bioethanol and bio-based chemical production. Biocatal. Agric. Biotechnol. 69, 103769 (2025). https://www.sciencedirect.com/science/article/pii/S1878818125002828
Usman, A. G., Jibrin, A. M., Mati, S. & Abba, S. I. Evidential-bio-inspired algorithms for modeling groundwater total hardness: A pioneering implementation of evidential neural network for feature selection in water resources management. Environ Chem Ecotoxicol [Internet]. ; (2025). Available from: https://www.sciencedirect.com/science/article/pii/S2590182625000219
Al-Omran, A. M., Mousa, M. A., AlHarbi, M. M. & Nadeem, M. E. A. Hydrogeochemical characterization and groundwater quality assessment in Al-Hasa, Saudi Arabia. Arab. J. Geosci. 11, 1–12 (2018).
El-Mahmoudi, A. S., Hussein, A. A. & Hofouf, A. Hydrochemical studies of groundwater at al Hassa Oasis, Eastern Region, Saudi Arabia. Adsorption 6 (1), 20–32 (2017).
Yassin, M. A. et al. Toward decontamination in coastal regions: groundwater Quality, Fluoride, Nitrate, and human health risk assessments within Multi-Aquifer Al-Hassa, Saudi Arabia. Water (Switzerland) 16(10), 1401. https://doi.org/10.3390/w16101401 (2024).
Al-Naeem, A. A. Evaluation of groundwater of Al-Hassa oasis, Eastern region Saudi Arabia. Res. J. Environ. Sci. 5 (7), 624 (2011).
Abba, S. I. et al. Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence. Sustainability. 15(21): 15655. (2023). https://www.mdpi.com/2071-1050/15/21/15655
Egbueri, J. C., Agbasi, J. C., Ayejoto, D. A., Khan, M. I. & Khan, M. Y. A. Extent of anthropogenic influence on groundwater quality and human health-related risks: an integrated assessment based on selected physicochemical characteristics. Geocarto Int. 38 (1), 2210100 (2023).
Nohara, Y., Matsumoto, K., Soejima, H. & Nakashima, N. Explanation of machine learning models using Shapley additive explanation and application for real data in hospital. Comput. Methods Programs Biomed. 214, 106584 (2022). https://www.sciencedirect.com/science/article/pii/S0169260721006581
Mangalathu, S., Hwang, S. H. & Jeon, J. S. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng. Struct. 219, 110927. https://doi.org/10.1016/j.engstruct.2020.110927 (2020).
Abba, S. I. et al. Drinking water resources suitability assessment based on pollution index of groundwater using improved explainable artificial intelligence. Sustainability 15 (21), 15655 (2023).
Ismail, A. I. H., Hassaballa, A. A., Almadini, A. M. & Daffalla, S. Analyzing the Spatial correspondence between different date fruit cultivars and farms’ cultivated Areas, case study: Al-Ahsa Oasis, Kingdom of Saudi Arabia. Appl. Sci. 12 (11), 5728 (2022).
Al Tokhais, A. S. & Rausch, R. The hydrogeology of Al Hassa springs. In: Proceedings the 3rdInternational Conference on Water Resources and Arid Environments and the. 16–9. (2008).
Yassin, M. A. et al. Integrating experimental-based vulnerability mapping with intelligent identification of multi-aquifer groundwater salinization. Next Sustain. 5: 100115. (2025). Available from: https://www.sciencedirect.com/science/article/pii/S2949823625000182
Chen, J., He, Y., Liang, Y., Wang, W. & Duan, X. Estimation of gross calorific value of coal based on the cubist regression model. Sci. Rep. 14 (1), 23176 (2024).
Steele, B., Chandler, J., Reddy, S. & Linear Regression Methods BT – Algorithms for Data Science. In: Steele B, Chandler J, Reddy S, editors. Cham: Springer International Publishing; 161–215. (2016). Available from: https://doi.org/10.1007/978-3-319-45797-0_6
Svetnik, V. et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43 (6), 1947–1958 (2003).
Breiman, L. Random forests. Mach. Learn. 45 (1), 5–32 (2001).
Lee, T. H., Ullah, A. & Wang, R. Bootstrap Aggregating and Random Forest BT – Macroeconomic Forecasting in the Era of Big Data: Theory and Practice. In: Fuleky P, editor. Cham: Springer International Publishing; 389–429. (2020). Available from: https://doi.org/10.1007/978-3-030-31150-6_13
Khalilia, M., Chakraborty, S. & Popescu, M. Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inf. Decis. Mak. 11, 1–13 (2011).
Idris, A., Rizwan, M. & Khan, A. Churn prediction in Telecom using random forest and PSO based data balancing in combination with various feature selection strategies. Comput. Electr. Eng. 38 (6), 1808–1819 (2012). https://www.sciencedirect.com/science/article/pii/S004579061200167X
Daning, C., Shigang, L., Yunquan, Z. & Asynch-SGBDT Train Stochastic Gradient Boosting Decision Trees in an Asynchronous Parallel Manner. In: 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 256–67. (2023).
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29 (5), 1189–1232 (2001).
Ilhan, E., Turali, M. Y. & Kozat, S. S. Gradient boosting with Moving-Average terms for nonlinear sequential regression. IEEE Signal. Process. Lett. 30, 1182–1186 (2023).
Ayyadevara, V. K. & Gradient Boosting Machine, B. T. – Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R. In: Ayyadevara VK, editor. Berkeley, CA: Apress; 117–34. (2018). Available from: https://doi.org/10.1007/978-1-4842-3564-5_6
Xia, L. Optimization of Performance Management for Commercial Companies by Integrating ROF and Light GBM Algorithms. In: 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE). 1–6. (2023).
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory. 13 (1), 21–27 (1967).
Kiran, B. V. S. et al. Enhancing Accident Prediction Through Integrated KNN and DBSCAN Algorithms for Superior Accuracy. In: 2024 8th International Conference on Inventive Systems and Control (ICISC). 423–8. (2024).
Nimon Kim, F. & Oswald Frederick, L. Understanding the results of multiple linear regression: beyond standardized regression coefficients. Organ. Res. Methods. 16 (4), 650–674. https://doi.org/10.1177/1094428113493929 (2013).
Belgiu, M. & Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens. 114: 24–31. (2016). https://www.sciencedirect.com/science/article/pii/S0924271616000265
Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front Neurorobot. 7(DEC). (2013).
Konstantinov, A. V. & Utkin, L. V. Interpretable machine learning with an ensemble of gradient boosting machines. Knowledge-Based Syst. 222: 106993. (2021). https://www.sciencedirect.com/science/article/pii/S0950705121002562
Kramer, O. & K-Nearest Neighbors BT – Dimensionality Reduction with Unsupervised Nearest Neighbors. In: Kramer O, editor. Berlin, Heidelberg: Springer Berlin Heidelberg; 13–23. (2013). Available from: https://doi.org/10.1007/978-3-642-38652-7_2
Sun, S. & Huang, R. An adaptive k-nearest neighbor algorithm. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery. 91–4. (2010).
Lundberg, S. & Lee, S. I. A Unified Approach to Interpreting Model Predictions. (2017).
Jibrin, A. M. et al. Influence of membrane characteristics and operational parameters on predictive control of permeance and rejection rate using explainable artificial intelligence (XAI). Anal. Var. ; (2025). 2(December 2024).
Nohara, Y., Inoguchi, T., Nojiri, C. & Nakashima, N. Explanation of machine learning models of colon cancer using SHAP considering interaction effects. arXiv preprint arXiv:2208.03112. http://arxiv.org/abs/2208.03112 (2022).
Bentéjac, C., Csörgő, A. & Martínez-Muñoz, G. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev. 54, 1937–1967 (2021).
Jibrin, A. M. et al. Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization. Environ. Sci. Pollut. Res. (2024). Available from: https://doi.org/10.1007/s11356-024-34716-6
Alam, S. M. K., Li, P., Rahman, M., Fida, M. & Elumalai, V. Key factors affecting groundwater nitrate levels in the Yinchuan Region, Northwest China: Research using the eXtreme Gradient Boosting (XGBoost) model with the SHapley Additive exPlanations (SHAP) method. Environ. Pollut. 364, 125336 (2025).
Zhang, T. et al. Evaluating the importance of vertical environmental variables for albacore fishing grounds in tropical Atlantic Ocean using machine learning and Shapley additive explanations (SHAP) approach. Fish Oceanogr 34(1), e12701 (2025).
Xu, Y. et al. Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization. Ecol. Indic. 166, 112543 (2024). https://www.sciencedirect.com/science/article/pii/S1470160X24010008
Ali, S. et al. The ecotoxicological and interactive effects of chromium and aluminum on growth, oxidative damage and antioxidant enzymes on two barley genotypes differing in al tolerance. Environ. Exp. Bot. 70 (2), 185–191 (2011). https://www.sciencedirect.com/science/article/pii/S0098847210001814
Zuo, H. et al. Distribution and risk assessment of metals in surface water and sediment in the upper reaches of the yellow River, China. Soil. Sediment. Contam. Int. J. 25 (8), 917–940. https://doi.org/10.1080/15320383.2016.1224995 (2016).
Bon, A. F., Aoudou, S. D., Ndam, A. M., Bineli, E. A. & Fita, E. D. in Groundwater Pollution Index Evaluation Test Using Electrical Conductivity in a Semi-arid Quaternary Aquifer (Kousseri-Cameroon, Lake Chad Basin): Multivariate Statistical Analysis Approach BT – Advances in Sustainable and Environmental Hydrology, Hydroge. 295–297 (eds Chaminé, H. I., Barbieri, M., Kisi, O., Chen, M. & Merkel, B. J.) (Springer International Publishing, 2019).
Jamshidzadeh, Z., Ehteram, M. & Shabanian, H. Bidirectional long Short-Term memory (BILSTM) – Support vector machine: A new machine learning model for predicting water quality parameters. Ain Shams Eng. J. 15 (3), 102510 (2024). https://www.sciencedirect.com/science/article/pii/S2090447923003994
Abba, S. I. et al. Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa Oasis (Saudi Arabia) using spatial, chemometric and index-based techniques. Environ. Res. 249, 118320. https://doi.org/10.1016/j.envres.2024.118320 (2024).
