Relative humidity quantification using interpretable machine learning based- stacking approach: representative case study in Ethiopia DOI
Gebre Gelete, Tesfalem Abraham, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al.

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(6)

Published: May 9, 2025

Language: Английский

Artificial intelligence and machine learning approaches in composting process: A review DOI
Fulya Aydın Temel, Özge Cağcağ Yolcu, Nurdan Gamze Turan

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 370, P. 128539 - 128539

Published: Jan. 3, 2023

Language: Английский

Citations

49
Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams DOI

To‐Hung Tsui,

Mark C.M. van Loosdrecht, Yanjun Dai

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128445 - 128445

Published: Dec. 5, 2022

Language: Английский

Citations

55
Artificial intelligence technologies in bioprocess: Opportunities and challenges DOI

Yang Cheng,

Xinyu Bi,

Yameng Xu

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128451 - 128451

Published: Dec. 9, 2022

Language: Английский

Citations

41
A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach DOI Open Access
Ria Aniza, Wei‐Hsin Chen, Anélie Pétrissans

et al.

Environmental Pollution, Journal Year: 2023, Volume and Issue: 324, P. 121363 - 121363

Published: Feb. 28, 2023

Language: Английский

Citations

37
Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning DOI
Yalin Li,

Zhuangzhuang Xue,

Suyan Li

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 385, P. 129444 - 129444

Published: July 1, 2023

Language: Английский

Citations

26
Machine learning for sustainable organic waste treatment: a critical review DOI Creative Commons
Rohit Gupta,

Zahra Hajabdollahi Ouderji,

Uzma Uzma

et al.

npj Materials Sustainability, Journal Year: 2024, Volume and Issue: 2(1)

Published: April 8, 2024

Abstract Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven techniques for treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, k -nearest neighbors. The application these explored terms their capacity complex processes. Additionally, the delves into physics-informed highlighting significance integrating domain knowledge improved model consistency. Comparative analyses are carried out to provide insights strengths weaknesses each technique, aiding practitioners selecting appropriate models diverse applications. Transfer learning specialized network variants also discussed, offering avenues enhancing predictive capabilities. work contributes valuable field modeling, emphasizing importance understanding nuances technique informed decision-making various treatment scenarios.

Language: Английский

Citations

14
Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning DOI
Bing Bai, Lixia Wang,

Fachun Guan

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 471, P. 134392 - 134392

Published: April 23, 2024

Language: Английский

Citations

13
Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges DOI

Li-ting Huang,

Jia-yi Hou,

Hongtao Liu

et al.

Waste Management, Journal Year: 2024, Volume and Issue: 178, P. 155 - 167

Published: Feb. 24, 2024

Language: Английский

Citations

12
Exploring sludge yield patterns through interpretable machine learning models in China's municipal wastewater treatment plants DOI
Y. Hu,

Renke Wei,

Ke Yu

et al.

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 204, P. 107467 - 107467

Published: Feb. 16, 2024

Language: Английский

Citations

11
Predicting maturity and identifying key factors in organic waste composting using machine learning models DOI
Ning Wang, Wanli Yang, Bingshu Wang

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 400, P. 130663 - 130663

Published: April 6, 2024

Language: Английский

Citations

9