A systematic evaluation of advanced machine learning models for nickel contamination management in soil using spectral data DOI Creative Commons

Kechao Li,

Tao Hu, Min Zhou

et al.

Journal of Hazardous Materials Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100576 - 100576

Published: Dec. 1, 2024

Language: Английский

Prediction of soil arsenic concentration in European soils: a dimensionality reduction and ensemble learning approach DOI Creative Commons
Mohammad Sadegh Barkhordari, Chongchong Qi

Journal of Hazardous Materials Advances, Journal Year: 2025, Volume and Issue: 17, P. 100604 - 100604

Published: Jan. 15, 2025

Language: Английский

Citations

1
Artificial Neural Networks for the Simulation and Modeling of the Adsorption of Fluoride Ions with Layered Double Hydroxides DOI Creative Commons
Julio Cesar Estrada-Moreno, Eréndira Rendón, M. L. Jiménez‐Núñez

et al.

Physchem, Journal Year: 2025, Volume and Issue: 5(1), P. 5 - 5

Published: Jan. 23, 2025

Adsorption is a complex process since it affected by multiple variables related to the physicochemical properties of adsorbate, adsorbent and interface; therefore, understand adsorption in batch systems, kinetics, isotherms empiric models are commonly used. On other hand, artificial neural networks (ANNs) have proven be useful solving wide variety problems science engineering due their combination computational efficiency precision results; for this reason, recent years, ANNs begun used describing processes. In work, we present an ANN model fluoride ions water with layered double hydroxides (LDHs) its comparison empirical kinetic models. LHD was synthesized characterized using X-Ray diffraction, FT-Infrared spectroscopy, BET analyses zero point charge. Fluoride ion evaluated under different experimental conditions, including contact time, initial pH concentration. A total 262 experiments were conducted, resulting data training testing model. The results indicate that can accurately forecast conditions determination coefficient R2 0.9918.

Language: Английский

Citations

0
Determining whether biochar can effectively increase crop yields: A machine learning model development with imbalanced data DOI Creative Commons
Weidong Jiao, Kechao Li, Min Zhou

et al.

Environmental Technology & Innovation, Journal Year: 2025, Volume and Issue: unknown, P. 104154 - 104154

Published: March 1, 2025

Language: Английский

Citations

0
Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland DOI
Jan Skála, Daniel Žížala, Robert Minařík

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 125035 - 125035

Published: March 24, 2025

Language: Английский

Citations

0
Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach DOI
Tao Hu, Kechao Li,

Chundi Ma

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 363, P. 142697 - 142697

Published: June 24, 2024

Language: Английский

Citations

3
Prediction of copper contamination in soil across EU using spectroscopy and machine learning: handling class imbalance problem DOI Creative Commons
Chongchong Qi,

Nana Zhou,

Tao Hu

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100728 - 100728

Published: Dec. 1, 2024

Language: Английский

Citations

0
A systematic evaluation of advanced machine learning models for nickel contamination management in soil using spectral data DOI Creative Commons

Kechao Li,

Tao Hu, Min Zhou

et al.

Journal of Hazardous Materials Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100576 - 100576

Published: Dec. 1, 2024

Language: Английский

Citations

0