Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas
The NGRB model yields the best predictive performances with R<sup>2</sup> of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO<sub>2</sub> flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO<sub>2</sub> emissions from underground coal fires, contributing to global efforts in combating climate change.
Authors
- Hung Vo Thanh (ORCID: https://orcid.org/0000-0002-7094-9380)
- Laith Abualigah (ORCID: https://orcid.org/0000-0002-2203-4549)
- Mohammad Sh. Daoud (ORCID: https://orcid.org/0000-0003-2682-9231)
- Mingze Guo (ORCID: https://orcid.org/0009-0005-7350-3172)
- Hemeng Zhang
- Qian Zheng (ORCID: https://orcid.org/0000-0003-0238-3220)
- Xiaoming Zhang (ORCID: https://orcid.org/0000-0003-2671-2850)
- Yongjun Wang (ORCID: https://orcid.org/0000-0003-4846-4130)
- Xiaoying Liu (ORCID: https://orcid.org/0000-0002-1751-7538)
Institutions
- Al Ain University (AE)
- Liaoning Technical University (CN)
- Al al-Bayt University (JO)
- Chitkara University (IN)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1016/j.jare.2024.10.034
- Citations
- 12
- Primary Topic
- Coal Properties and Utilization
- Type
- article
- Field-Weighted Citation Impact
- 4.03
Funders
- National Natural Science Foundation of China