Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models DOI Creative Commons
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬,

Farah Loui Alhalimi

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 18, 2025

The contamination of water and soils with heavy metals poses a significant environmental threat, making the development effective removal strategies global priority. Hence, determination can play an essential role in monitoring assessment. In current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient (GB), HistGradientBoosting, Extreme (XGBoost), Light Gradient-Boosting Machine (LightGBM)) were applied attempt to predict adsorption efficiency several Pb, Cd, Ni, Cu, Zn) according different factors including temperature, pH, biochar characteristics. Data collected from open-source literature review 353 samples. At first stage, data processing was performed outliers' scaling for better modeling applicability; whereas, second stage predictive conducted. results showed that XGBoost model attained superior accuracy comparison other by achieving highest coefficient (R2 = 0.92). research extended investigate feature importance analysis which indicated initial concentration ratio pH most influential toward followed Pyrolysis while features like physical properties as surface area pore structure had minimal effect on efficiency. These findings highlighted using ML guiding solutions it provides efficient prediction ease selection application.

Language: Английский

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants DOI
Bingyou Liu,

F. Xi,

Huanjing Zhang

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 402, P. 130776 - 130776

Published: May 1, 2024

Language: Английский

Citations

9
Machine learning-based exploration of biochar for environmental management and remediation DOI
Burcu Oral,

Ahmet Coşgun,

M. Erdem Günay

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 360, P. 121162 - 121162

Published: May 14, 2024

Language: Английский

Citations

8
Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review DOI Open Access
Zhuang Liu, Yonghai Gan, Jun Luo

et al.

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 85 - 85

Published: Jan. 1, 2025

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding these chemicals’ fate, distribution, transport, and risk aquatic ecosystems. Modeling is useful approach for determining ECs’ characteristics their behaviors environments. This article proposes systematic taxonomy EC models addresses gaps the comprehensive analysis applications. The reviewed include conventional quality models, multimedia fugacity machine learning (ML) models. Conventional have higher prediction accuracy spatial resolution; nevertheless, they are limited functionality can only be used to predict contaminant concentrations Fugacity excellent at depicting how travel between different environmental media, but cannot directly analyze variations parts same media because model assumes that constant within compartment. Compared other ML applied more scenarios, such as identification assessments, rather than being confined concentrations. In recent years, with rapid development artificial intelligence, surpassed becoming one newest hotspots study ECs. primary challenge faced by outcomes difficult interpret understand, this influences practical value an some extent.

Language: Английский

Citations

1
Machine learning-driven prediction of nitrate-N adsorption efficiency by Fe-modified biochar: Refined model tuning and identification of crucial features DOI
Chen Li,

Xie Guixian,

Jing Li

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 107026 - 107026

Published: Jan. 22, 2025

Language: Английский

Citations

1
Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models DOI Creative Commons
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬,

Farah Loui Alhalimi

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 18, 2025

The contamination of water and soils with heavy metals poses a significant environmental threat, making the development effective removal strategies global priority. Hence, determination can play an essential role in monitoring assessment. In current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient (GB), HistGradientBoosting, Extreme (XGBoost), Light Gradient-Boosting Machine (LightGBM)) were applied attempt to predict adsorption efficiency several Pb, Cd, Ni, Cu, Zn) according different factors including temperature, pH, biochar characteristics. Data collected from open-source literature review 353 samples. At first stage, data processing was performed outliers' scaling for better modeling applicability; whereas, second stage predictive conducted. results showed that XGBoost model attained superior accuracy comparison other by achieving highest coefficient (R2 = 0.92). research extended investigate feature importance analysis which indicated initial concentration ratio pH most influential toward followed Pyrolysis while features like physical properties as surface area pore structure had minimal effect on efficiency. These findings highlighted using ML guiding solutions it provides efficient prediction ease selection application.

Language: Английский

Citations

1