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

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.

Digital economy, technological progress, and city export trade

“…Specifically, based on the digital economy index compiled by Katz and Koutroumpis [ 7 ], Hagsten and Kotnik [ 39 ], and the China Academy of Information and Communications Technology (2021), we select five dimensions to measure the digital economy, including total capita telecom business volume, total capita postal business volume, the number of employees in the information industry, the number of internet users and the number of mobile phone users at the end of the year. Then we use the entropy weight method to get a comprehensive score of the digital economy, which measures the development level of the digital economy in cities [ 40 ].…”

Comparison Reviews on E-commerce Research Methods: Suggestions for Post-COVID-19

“…The correlation analysis might confirm a strong relationship between the data (Wu et al, 2022). Such as correlation analysis could find a linear relationship between linear-dependent variables; if it existed: it might give a solid indicator to interpret a robust nonlinear relationship between nonlinear-dependent variables (AL-Rousan et al, 2021).…”

Prioritizing population-based nutrition-related interventions to prevent and control hypertension in Iran: a multi-criteria decision-making approach

“…Various MCDM techniques have been developed to estimate the weights of the criteria close to the preferences of the decision maker. Each MCDM method has been developed with different advantages and disadvantages, though the researchers usually select an approach based on the nature and intricacy of the problem [ 27 , 28 ]. There are no criteria for the effectiveness of weighing methods [ 27 , 29 ].…”

“…Each MCDM method has been developed with different advantages and disadvantages, though the researchers usually select an approach based on the nature and intricacy of the problem [ 27 , 28 ]. There are no criteria for the effectiveness of weighing methods [ 27 , 29 ]. From the literature review, it can be clearly noticed that different mathematical techniques have mainly been employed for two purposes: (a) determining the relative weights of the considered criteria by evaluating one against the others, and (b) ranking the candidate alternatives based on the accumulative score with respect to each criterion [ 30 ].…”

“…Then, a comparison is made between each of these two criteria (best and worst) with the other ones. The main advantages of BWM method include achieving better consistency in pairwise comparisons and more reliable weight results due to the use of fewer comparison data [ 27 , 37 ]. However, this model is unacceptable to many researchers as solving non-linear models makes the application of the BWM significantly more complex [ 33 ].…”