Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas

IF 11.4 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Journal of Advanced Research Pub Date : 2024-11-07 DOI:10.1016/j.jare.2024.10.034
Yongjun Wang, Mingze Guo, Hung Vo Thanh, Hemeng Zhang, Xiaoying Liu, Qian Zheng, Xiaoming Zhang, Mohammad Sh. Daoud, Laith Abualigah
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Abstract

Introduction

Underground coal fires pose significant environmental and health risks due to releasing CO2 emissions. Predicting surface CO2 flux accurately in underground coal fire areas is crucial for understanding the distribution of spontaneous combustion zones and developing effective mitigation strategies. In recent years, advanced machine learning techniques have shown promise in various carbon-related studies. This research uses an experimental approach to explore the power of advanced machine learning schemes for predicting CO2 flux in underground coal fire areas.

Objectives

By leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies.

Methods

The study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics.

Results

The NGRB model yields the best predictive performances with R2 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 CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.

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基于地下煤火区实验方法的二氧化碳通量预测先进机器学习方案
引言 地下煤火会释放出二氧化碳,对环境和健康构成重大威胁。准确预测地下煤火区的地表二氧化碳通量对于了解自燃区的分布和制定有效的缓解策略至关重要。近年来,先进的机器学习技术在各种与碳有关的研究中大显身手。通过利用先进的机器学习方案和实验方法,本研究旨在为煤火区二氧化碳通量预测提供有价值的见解,并为环境监测和管理策略提供信息。方法本研究涉及收集地下煤火区特定的实验数据集,包括与二氧化碳通量和地下煤火特征相关的各种参数。采用创新的特征工程技术来捕捉地下煤火区的独特特征及其对二氧化碳通量的影响。评估和比较了不同机器学习算法的预测能力,包括自然梯度提升回归(NGRB)、极端梯度提升(XGboost)、轻梯度提升(LGRB)和随机森林(RF)。对这些模型进行了训练、优化,并使用适当的性能指标进行了评估。这项研究的新贡献包括开发了针对地下煤火区的精确预测模型,揭示了驱动二氧化碳通量的潜在因素。研究结果对划定自燃区和减少地下煤火的二氧化碳排放具有实际意义,有助于全球应对气候变化。
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来源期刊
Journal of Advanced Research
Journal of Advanced Research Multidisciplinary-Multidisciplinary
CiteScore
21.60
自引率
0.90%
发文量
280
审稿时长
12 weeks
期刊介绍: Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences. The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.
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