Coal mine area monitoring method by machine learning and multispectral remote sensing images

He, Dakuo; Le, Ba Tuan; Xiao, Dong; Mao, Yachun; Shan, Feng; Ha, Thai Thuy Lam

doi:10.1016/j.infrared.2019.103070

Cited by 31 publications

(13 citation statements)

References 40 publications

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“…In the area of land surface detection, change detection [109][110][111][112][113] and land cover mapping [114][115][116][117] studies were performed to detect the land surface that changed during mine operation, and to classify the surface composition, respectively.…”

Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

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Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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“…Remote sensing can also help monitor the dynamic distribution of coal and gangue. The spectral reflectance characteristics of coal and gangue were first calculated by machine learning from a number of positive and negative samples, and then used to extract the distribution of coal and gangue in real time [106]. A typical application case was the Heidaigou coal mine in Northwestern China (Figure 9).…”

Section: Remote Sensing Monitoring Of Miningareasmentioning

confidence: 99%

“…Application of the tree root based multi-layer extreme learning machine model in opencast Heidaigou coal mine area[106].…”

mentioning

confidence: 99%

A Review of Remote Sensing for Environmental Monitoring in China

et al. 2020

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The natural environment is essential for human survival and development since it provides water resources, land resources, biological resources and climate resources etc. As a developing country, China has witnessed a significant change in the natural environment in recent decades; and therefore, monitoring and mastering the status of the environment is of great significance. Due to the characteristics of large-scale and dynamic observation, remote sensing technology has been an indispensable approach for environmental monitoring. This paper reviews the satellite resources, institutions and policies for environmental monitoring in China, and the advances in research and application of remote sensing from five aspects: ecological index retrieval, environmental monitoring in protected areas, rural areas, urban areas and mining areas. The remote sensing models and methods for various types of environmental monitoring, and the specific applications in China are comprehensively summarized. This paper also points out major challenges existing at the current stage: satellite sensor problems, integrated use challenges of datasets, uncertainty in the retrieval process of ecological variables, scaling effect problems, a low degree of automation, the weak ability of forecasting and comprehensive analysis, and a lack of computational power for massive datasets. Finally, the development trend and future directions are put forward to direct the research and application of environmental monitoring and protection in the new era.

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“…Plenty of studies have shown that goaf water is produced as a result of the oxidation of sulfur-rich coal or gangue/spoils (He et al 2019;Zhu et al 2020) such as pyrite (FeS 2 ) (Lazareva et al 2019), and subsequent contraction with water for the formation of sulfuric acid (Mokgehle et al 2019). Basically, goaf water is characterized by low pH value.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Solidification performances of contaminants by red mud-based cementitious paste filling material and leaching behavior of contaminants in different pH and redox potential environments

Zhao

Zhang²,

Zhao

et al. 2022

Water Science and Technology

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Considering the urgent need for disposal of red mud and the comprehensive treatment of coal mined-out areas, this paper presented red mud-based cementitious paste filling material (RMFM) to achieve the purpose of green filling treatment. However, the solidification performance of alkaline RMFM for contaminants can be affected when in contact with acid goaf water in practice, which may in turn causes secondary pollution to the surroundings. The leaching tests of RMFM under different pH and redox potential (Eh) conditions were designed to investigate the effects of environmental elements on the solidification performance of RMFM, and primarily investigated the treatment effectiveness of RMFM on goaf water. The test results manifest that the acidic and oxidizing environments could damage the hydration products generated by alkali and sulfate activation, thus affecting the solidification performance, while the alkaline and reducing environments could effectively prevent the release of the contaminants by enhancing the degree of alkali activation and inhibiting oxidation acid forming process. In the possible exposure environment, RMFM could effectively stabilize its own pollutants without secondary pollution. In addition, the powder RMFM samples had significant removal effects on heavy metals, the values of Cu, Pb, and As removal efficiency all reached more than 96.15%.

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Coal mine area monitoring method by machine learning and multispectral remote sensing images

Cited by 31 publications

References 40 publications

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

A Review of Remote Sensing for Environmental Monitoring in China

Solidification performances of contaminants by red mud-based cementitious paste filling material and leaching behavior of contaminants in different pH and redox potential environments

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