Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models

Iqbal, Javed; Su, Chunli; Ahmad, Maqsood; Baloch, Muhammad Yousuf Jat; Rashid, Abdur; Ullah, Zahid; Abbas, Hasnain; Nigar, Anam; Ali, Asmat; Ullah, Arif

doi:10.1007/s10653-023-01782-7

Environ Geochem Health

2023

DOI: 10.1007/s10653-023-01782-7

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Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models

Javed Iqbal,

Chunli Su,

Maqsood Ahmad

et al.

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2024

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Cited by 4 publications

References 102 publications

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Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Li,

Liang,

Wang

et al. 2024

Environ Geochem Health

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Groundwater nitrate contamination poses a potential threat to human health and environmental safety globally. This study proposes an interpretable stacking ensemble learning (SEL) framework for enhancing and interpreting groundwater nitrate spatial predictions by integrating the two-level heterogeneous SEL model and SHapley Additive exPlanations (SHAP). In the SEL model, five commonly used machine learning models were utilized as base models (gradient boosting decision tree, extreme gradient boosting, random forest, extremely randomized trees, and k-nearest neighbor), whose outputs were taken as input data for the meta-model. When applied to the agricultural intensive area, the Eden Valley in the UK, the SEL model outperformed the individual models in predictive performance and generalization ability. It reveals a mean groundwater nitrate level of 2.22 mg/L-N, with 2.46% of sandstone aquifers exceeding the drinking standard of 11.3 mg/L-N. Alarmingly, 8.74% of areas with high groundwater nitrate remain outside the designated nitrate vulnerable zones. Moreover, SHAP identified that transmissivity, baseflow index, hydraulic conductivity, the percentage of arable land, and the C:N ratio in the soil were the top five key driving factors of groundwater nitrate. With nitrate threatening groundwater globally, this study presents a high-accuracy, interpretable, and flexible modeling framework that enhances our understanding of the mechanisms behind groundwater nitrate contamination. It implies that the interpretable SEL framework has great promise for providing valuable evidence for environmental management, water resource protection, and sustainable development, particularly in the data-scarce area. Supplementary Information The online version contains supplementary material available at 10.1007/s10653-024-02201-1.

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Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Li,

Liang,

Wang

et al. 2024

Environ Geochem Health

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Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria

Boudibi,

Fadlaoui,

Hiouani

et al. 2024

Environ Sci Pollut Res

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Aquifer characterization using geophysical borehole-logging and hydrochemical techniques—a case study from Lahore, Pakistan

Ahmed,

Ismail,

Khan

2024

Environ Earth Sci

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Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models

Cited by 4 publications

References 102 publications

Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria

Aquifer characterization using geophysical borehole-logging and hydrochemical techniques—a case study from Lahore, Pakistan

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