Visual analysis of coal fire detection research based on bibliometrics

ABSTRACT

Accurate detection of coal fires is crucial for effective fire control planning. To enhance our comprehension of the global landscape of coal fire technology research and to propel theoretical advancements in coal fire prevention and control, this study conducted a comprehensive analysis of 1036 papers from the Web of Science core database and Scopus. Employing bibliometric methods and knowledge mapping tools like Vosviewer and Citespace, the analysis encompassed various aspects including annual distribution, geographical distribution, institutional distribution, main journals, core literature, core authors, and research hot topics. The results reveal that coal fire detection technology research has experienced a significant increase in the past decade. The most active countries in this field are China, the United States, India, Germany, Spain, Australia, United Kingdom, Poland, Canada, and Brazil, with the US having the greatest influence. Most of the leading research institutions are based in China. The main journals publishing research in this area are Fuel, Energy & Fuels, International Journal of Coal Geology, International Journal of Remote Sensing, and Remote Sensing. At present, the underlying theories and research frameworks in this field are well-established, but there is a lack of forward-looking studies. The main objectives of coal fire detection research are the development of fine data processing techniques and high-precision detection equipment. The research frontier in this field includes remote sensing technology, satellite sensors, coal fire physical models, and algorithmic programming.

KEYWORDS:

• Coal fire
• detection
• bibliometrics
• knowledge mapping
• visualization

Acknowledgements

Funding for this work was provided by the National Natural Science Foundation of China (No.51774290) and the Project of Education Department of Guizhou province (KY [2019]164)..

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [51774290]; Project of Education Department of Guizhou province [(KY [2019]164].