Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
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2026
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| author | Ansah-Narh, T. Afrifa, G. Y. Tandoh, J. B. Asare, K. Addi, M. Yorke, K. E. Akpoley, D. M. A. Aidoo, K. Fosuhene, S. K. |
| author_facet | Ansah-Narh, T. Afrifa, G. Y. Tandoh, J. B. Asare, K. Addi, M. Yorke, K. E. Akpoley, D. M. A. Aidoo, K. Fosuhene, S. K. |
| contents | Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed and affected by correlated contaminants, leading to biased predictions without transformation. This study develops a predictive framework integrating response transformations with nested cross-validated ensemble machine learning. Three transformations (raw, log, and Gaussian copula) were applied to HPI and evaluated across six learners: support vector regression (SVM), $k$-nearest neighbours (k-NN), CART, Elastic Net, kernel ridge regression, and a stacked Lasso ensemble. Raw-scale models produced deceptively high fits (Elastic Net and stacked ensemble $R^2 \approx 1.0$), suggesting over-optimism. The log transformation stabilised variance (SVM: $R^2 = 0.93$, RMSE $= 0.18$; k-NN: $R^2 = 0.92$, RMSE $= 0.20$). The Gaussian copula gave the most reliable results: stacked ensemble $R^2 = 0.96$ (RMSE $= 0.19$), with other learners maintaining high accuracy. Copula-based models improved residuals and produced spatially plausible maps. DBSCAN clustering revealed Fe and Mn as primary HPI contributors, consistent with regional hydrogeochemistry. Limitations include reliance on random (not spatial) cross-validation and basin-specific scope. Future work should explore spatial validation and other geological settings. Overall, distribution-aware ensembles with clustering diagnostics offer robust, interpretable assessments of groundwater contamination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00056 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution Ansah-Narh, T. Afrifa, G. Y. Tandoh, J. B. Asare, K. Addi, M. Yorke, K. E. Akpoley, D. M. A. Aidoo, K. Fosuhene, S. K. Machine Learning Artificial Intelligence Data Analysis, Statistics and Probability Geophysics Applications Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed and affected by correlated contaminants, leading to biased predictions without transformation. This study develops a predictive framework integrating response transformations with nested cross-validated ensemble machine learning. Three transformations (raw, log, and Gaussian copula) were applied to HPI and evaluated across six learners: support vector regression (SVM), $k$-nearest neighbours (k-NN), CART, Elastic Net, kernel ridge regression, and a stacked Lasso ensemble. Raw-scale models produced deceptively high fits (Elastic Net and stacked ensemble $R^2 \approx 1.0$), suggesting over-optimism. The log transformation stabilised variance (SVM: $R^2 = 0.93$, RMSE $= 0.18$; k-NN: $R^2 = 0.92$, RMSE $= 0.20$). The Gaussian copula gave the most reliable results: stacked ensemble $R^2 = 0.96$ (RMSE $= 0.19$), with other learners maintaining high accuracy. Copula-based models improved residuals and produced spatially plausible maps. DBSCAN clustering revealed Fe and Mn as primary HPI contributors, consistent with regional hydrogeochemistry. Limitations include reliance on random (not spatial) cross-validation and basin-specific scope. Future work should explore spatial validation and other geological settings. Overall, distribution-aware ensembles with clustering diagnostics offer robust, interpretable assessments of groundwater contamination. |
| title | Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution |
| topic | Machine Learning Artificial Intelligence Data Analysis, Statistics and Probability Geophysics Applications |
| url | https://arxiv.org/abs/2605.00056 |