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Sakizadeh M, Zhang C, Milewski A. Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:80. [PMID: 38367130 DOI: 10.1007/s10653-023-01845-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 12/21/2023] [Indexed: 02/19/2024]
Abstract
Combining the results of base models to create a meta-model is one of the ensemble approaches known as stacking. In this study, stacking of five base learners, including eXtreme gradient boosting, random forest, feed-forward neural networks, generalized linear models with Lasso or Elastic Net regularization, and support vector machines, was used to study the spatial variation of Mn, Cd, Pb, and nitrate in Qom-Kahak Aquifers, Iran. The stacking strategy proved to be an effective substitute predictor for existing machine learning approaches due to its high accuracy and stability when compared to individual learners. Contrarily, there was not any best-performing base model for all of the involved parameters. For instance, in the case of cadmium, random forest produced the best results, with adjusted R2 and RMSE of 0.108 and 0.014, as opposed to 0.337 and 0.013 obtained by the stacking method. The Mn and Cd showed a tight link with phosphate by the redundancy analysis (RDA). This demonstrates the effect of phosphate fertilizers on agricultural operations. In order to analyze the causes of groundwater pollution, spatial methodologies can be used with multivariate analytic techniques, such as RDA, to help uncover hidden sources of contamination that would otherwise go undetected. Lead has a larger health risk than nitrate, according to the probabilistic health risk assessment, which found that 34.4% and 6.3% of the simulated values for children and adults, respectively, were higher than HQ = 1. Furthermore, cadmium exposure risk affected 84% of children and 47% of adults in the research area.
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Affiliation(s)
- Mohamad Sakizadeh
- Department of Environmental Sciences, Shahid Rajaee Teacher Training University, Lavizan, 1678815811, Tehran, Iran.
| | - Chaosheng Zhang
- International Network for Environment and Health (INEH), School of Geography, Archaeology and Irish Studies, University of Galway, Galway, Ireland
| | - Adam Milewski
- Department of Geology, University of Georgia, Athens, USA
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Elzain HE, Abdalla O, A Ahmed H, Kacimov A, Al-Maktoumi A, Al-Higgi K, Abdallah M, Yassin MA, Senapathi V. An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119896. [PMID: 38171121 DOI: 10.1016/j.jenvman.2023.119896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024]
Abstract
Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.
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Affiliation(s)
- Hussam Eldin Elzain
- Water Research Center, Sultan Qaboos University, P.O. 50, Al Khoudh 123, Oman.
| | - Osman Abdalla
- Department of Earth Sciences, College of Science, Sultan Qaboos University, P.O. 36, Al Khoudh 123, Oman.
| | - Hamdi A Ahmed
- Department of Industrial and Data Engineering, Pukyong National University, Busan, 48513, South Korea.
| | - Anvar Kacimov
- Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, P.O. 34, Al Khoudh 123, Oman.
| | - Ali Al-Maktoumi
- Water Research Center, Sultan Qaboos University, P.O. 50, Al Khoudh 123, Oman; Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, P.O. 34, Al Khoudh 123, Oman.
| | - Khalifa Al-Higgi
- Department of Earth Sciences, College of Science, Sultan Qaboos University, P.O. 36, Al Khoudh 123, Oman.
| | - Mohammed Abdallah
- College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China.
| | - Mohamed A Yassin
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
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Wang Z, Wang Y. Groundwater quality assessment by multi-model comparison: a comprehensive study during dry and wet periods in semi-arid regions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:51571-51594. [PMID: 36810824 DOI: 10.1007/s11356-023-25937-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
With the impact of human engineering activities, groundwater pollution has seriously threatened the health of human life. Accurate water quality assessment is the basis of controlling groundwater pollution and improving groundwater management, especially in specific regions. A typical semi-arid city in Fuxin Province of China is taken as an example. We use remote sensing and GIS to compile four environmental factors, such as rainfall, temperature, LULC, and NDVI, to analyze and screen the correlation of indicators. The differences among the four algorithms were compared by using hyperparameters and model interpretability, including random forest (RF), support vector machine support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). The groundwater quality of the city during the dry and wet periods was comprehensively evaluated. The results show that the RF model has higher integrated precision (MSE = 0.11, 0.035; RMSE = 0.19,0.188; R2 = 0.829,0.811; ROC = 0.98, 0.98). The quality of shallow groundwater is poor in general, 29%, 38%, 33% of the groundwater quality in low-water period is III, IV, V water. Thirty-three percent and 67% of the groundwater quality in the high-water period were IV and V water. The proportion of poor water quality in high-water period was higher than that in low-water period, which was consistent with the actual investigation. This study provides a kind of machine learning method for the semi-arid area, which cannot only promote the sustainable development of groundwater in this area, but also provide reference for the management policy of related departments.
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Affiliation(s)
- Zihan Wang
- College of Mining, Liaoning Technical University, Zhonghua Road 47, Fuxin, 123000, China
| | - Yong Wang
- College of Mining, Liaoning Technical University, Zhonghua Road 47, Fuxin, 123000, China.
- School of Municipal and Environmental Engineering, Henan University of Urban Construction, Longxiang Road, Pingdingshan, 467036, China.
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