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
Estimation of soil properties using Hyperspectral imaging and Machine Learning DOI Creative Commons

Eirini Chlouveraki,

Nikolaos Katsenios,

Aspasia Euthimiadou

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100790 - 100790

Published: Jan. 1, 2025

Language: Английский

Citations

1
Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites DOI Creative Commons
Bin Feng, Shahab Hosseini, Jie Chen

et al.

Infrastructures, Journal Year: 2024, Volume and Issue: 9(10), P. 181 - 181

Published: Oct. 9, 2024

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such fly ash and ground granulated blast furnace slag (GGBS). The accurate their is crucial for optimizing mix design reducing experimental efforts. We present a comparative analysis two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) Sine Cosine Algorithm (SCA-RF), against traditional regression methods classical models like Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), Radial Basis Function (RBF). Using comprehensive dataset derived from various scientific publications, we focus on key input variables including fine aggregate, GGBS, ash, sodium hydroxide (NaOH) molarity, others. Our results indicate that SCA-RF model achieved superior performance root mean square error (RMSE) 1.562 coefficient determination (R2) 0.987, compared HHO-RF model, which obtained an RMSE 1.742 R2 0.982. Both significantly outperformed methods, demonstrating higher reliability predicting GePC. research underscores potential advancing construction materials through precise predictive modeling, paving way more environmentally friendly efficient practices.

Language: Английский

Citations

7
Interpretable Machine Learning for Predicting Heavy Metal Removal Efficiency in Electrokinetic Soil Remediation DOI
Mohammad Sadegh Barkhordari,

Nana Zhou,

Kechao Li

et al.

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(6), P. 114330 - 114330

Published: Oct. 6, 2024

Language: Английский

Citations

4
Digital mapping of soil salinity with time-windows features optimization and ensemble learning model DOI Creative Commons
Shuaishuai Shi, Nan Wang, Songchao Chen

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102982 - 102982

Published: Dec. 1, 2024

Language: Английский

Citations

4
Application of machine learning to analyze Ohmic dissipative flow of $$\text{ZnO}{-}\text{SAE}50$$ nanofluid between two concentric cylinders DOI

Ghulam Haider,

Naveed Ahmed

The European Physical Journal Plus, Journal Year: 2025, Volume and Issue: 140(3)

Published: March 5, 2025

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
A Dynamic All-Dimensional Adaptive Particle Swarm Optimization Algorithm Prediction Method for the Height of the Water-Conducting Fracture in Complex Stress Environments DOI
Yingshun Li, Junmeng Li, Yanli Huang

et al.

Rock Mechanics and Rock Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Language: Английский

Citations

0
Comparative analysis of machine learning algorithms for identifying cobalt contamination in soil using spectroscopy DOI

Nana Zhou,

Tao Hu, Mengting Wu

et al.

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(5), P. 113328 - 113328

Published: June 14, 2024

Language: Английский

Citations

3
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