ArticleScientific reports2025
Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Article
- Human and environmental controls on soil contamination in a dust-prone region revealed by random forest and Shapley additive explanations analysis.Scientific reports · 2026Article
- Article
- Non-destructive prediction of carbonization indices in biochar derived from underutilized forest biomass using ATR-IR chemometric modeling.Scientific reports · 2026Article
- Integrating chemical modification pathways and machine learning for optimization of nitrate removal by rapeseed (Brassica napus L.) biochar.Scientific reports · 2025Article
- Assessment and prediction of copper release amount from copper oxide facepieces.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The contamination of water and soils with heavy metals poses a significant environmental threat, making the development of effective removal strategies a global priority. Hence, the determination of heavy metals can play an essential role in environmental monitoring and assessment. In the current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient Boosting (GB), HistGradientBoosting, Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM)) were applied in attempt to predict the adsorption efficiency of several heavy metals (i.e., Pb, Cd, Ni, Cu, and Zn) according to different factors including temperature, pH, and biochar characteristics. Data were collected from open-source literature review including 353 samples. At the first stage, data processing was performed including outliers' removal and scaling for better data modeling applicability; whereas, in the second stage the predictive models were conducted. The results showed that XGBoost model attained the superior accuracy in comparison with other models by achieving the highest determination coefficient (R
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Registered trials
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