Search
Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
Abstract
Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors’ own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption.
Citations
-
3
CrossRef
-
0
Web of Science
-
5
Scopus
Authors (9)
-
Van Giao Nguyen
-
Prabhakar Sharma
-
Ümit Ağbulut
-
Huu Son Le
-
Dao Nam Cao
-
Sameh M. Osman
-
Huu Cuong Le
-
Viet Dung Tran
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
International Journal of Green Energy
no. 21,
pages 2771 - 2798,
ISSN: 1543-5075 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Nguyen V. G., Sharma P., Ağbulut Ü., Le H. S., Cao D. N., Dzida M., Osman S. M., Le H. C., Tran V. D.: Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches// International Journal of Green Energy -Vol. 21,iss. 12 (2024), s.2771-2798
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/15435075.2024.2326076
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
seen 50 times
Recommended for you
Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
- G. V. Nguyen,
- P. Sharma,
- Ü. Ağbulut
- + 6 authors
Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
- V. N. N. Nhanh Van,
- W. Tarełko,
- S. Prabhakar
- + 5 authors
Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
- F. Bagherzadeh,
- A. Shojaei Nouri,
- M. J. Mehrani
- + 1 authors