A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Journal article


Wu, Robert M. X., Zhang, Zhongwu, Yan, Wanjun, Fan, Jianfeng, Gou, Jinwen, Liu, Bao, Gide, Ergun, Soar, Jeffrey, Shen, Bo, Fazal-e-Hasan, Syed, Liu, Zengquan, Zhang, Peng, Wang, Peilin, Cui, Xinxin, Peng, Zhanfei and Wang, Ya. (2022). A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement. PLoS ONE. 17(1), p. Article e0262261. https://doi.org/10.1371/journal.pone.0262261
AuthorsWu, Robert M. X., Zhang, Zhongwu, Yan, Wanjun, Fan, Jianfeng, Gou, Jinwen, Liu, Bao, Gide, Ergun, Soar, Jeffrey, Shen, Bo, Fazal-e-Hasan, Syed, Liu, Zengquan, Zhang, Peng, Wang, Peilin, Cui, Xinxin, Peng, Zhanfei and Wang, Ya
Abstract

Background
As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety.

Methods
Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee.

Results
The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020.

Conclusions
The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

Year2022
JournalPLoS ONE
Journal citation17 (1), p. Article e0262261
PublisherPublic Library of Science
ISSN1932-6203
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0262261
PubMed ID35085274
Scopus EID2-s2.0-85123694782
PubMed Central IDPMC8802816
Open accessPublished as ‘gold’ (paid) open access
Page range1-26
FunderShanxi Coking Coal Project
Shanxi Social Science Federation
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online27 Jan 2022
Publication process dates
Accepted21 Dec 2021
Deposited16 May 2023
Grant ID201809fx03
SSKLZDKT2019053
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