Risk assessment for long-distance gas pipelines in coal mine gobs based on structure entropy weight method and multi-step backward cloud transformation algorithm based on sampling with replacement

Liang, Xiaobin; Liang, Wei; Zhang, Laibin; Guo, Xiaofang

doi:10.1016/j.jclepro.2019.04.133

Cited by 59 publications

(24 citation statements)

References 25 publications

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“…Compared with other methods, the entropy method can effectively avoid the subjectivity of index weighting, and objectively conduct evaluations [30][31][32]. Therefore, referring to the literature above, the entropy method was used to calculate the comprehensive evaluation index of resilience of smart cities.…”

Section: The Entropy Evaluation Methodsmentioning

confidence: 99%

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

et al. 2020

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Based on the sample data of 81 non-provincial smart cities in China in 2017, the comprehensive evaluation index of the resilience of sample cities is calculated by using the entropy method, and the spatial differences of different factors on the resiliency are analyzed by using the geographical weighted regression (GWR) model. The conclusions are as follows: Firstly, the comprehensive evaluation index of the resilience of smart cities presents a spatial distribution characteristic of decreasing from the east to the west. At the same time, the resilience comprehensive index, the public infrastructure resilience capacity index, the economic development resilience index, the social security resilience index, and the ecological environment resilience index of smart cities have obvious agglomeration effects on their geographical spaces. Secondly, the public infrastructure resilience capacity index and the ecological environment resilience index are both low with a discrete distribution, while the economic development resilience index and the social security resilience index are both high with a concentrated distribution. Thirdly, different factors have significantly positive effects on the resilience of smart cities. In particular, the public infrastructure capacity resilience index decreases from the north to the south with the spatial distribution pattern of concentration, the economic development resilience index and the ecological environment resilience index of smart cities decrease from the east to the west with a concentrated spatial distribution pattern, and the social security resilience index of smart cities decreases from the southwest to the northeast with a concentrated spatial distribution pattern. Therefore, it is necessary to enhance awareness of smart cities, strengthen the driving force of science and technology innovation, strengthen public infrastructure and service construction, and continuously improve the rapid resilience of smart cities.

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Section: The Entropy Evaluation Methodsmentioning

confidence: 99%

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

et al. 2020

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“…In order to overcome the informational overlap between multiple index variables and the subjectivity of artificial weight determination, we adopted the Structure Entropy Weight (SEW) method and the Mean Squared Deviation (MSD) method to analyze the indicators' comprehensive weights. The basic principle of the SEW method was to rank the importance degree of each indicator and then use the entropy method to quantitatively analyze the uncertainty of the typical order [28,[39][40][41]. The MSD method, an objective weighting method, determined the weight according to the relative dispersion degree of the attribute value of each evaluation index [26,42,43].…”

Section: Theoretical Framework and Indicator Evaluation System Of Indmentioning

confidence: 99%

Investigating the Relationship between the Industrial Structure and Atmospheric Environment by an Integrated System: A Case Study of Zhejiang, China

et al. 2020

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Under the dual pressure of industrial structure upgrade and atmospheric environment improvement, China, in a transition period, is facing the challenge of coordinating the relationship between the industry and the environment system to promote the construction of a beautiful China. Based on system theory and coupling coordination model, the interaction analysis framework between industrial structure (IS) and atmospheric environment (AE) was constructed. An integrated system with 24 indicators was established by the pressure-state-response (PSR) model of IS and level-quality-innovation (LQI) model of AE. Then, we analyzed trends observed in coupling coordination degree (CCD) and dynamic coupling coordination degree (DCCD) for 11 cities in Zhejiang Province, China, using statistical panel data collected from 2006 to 2017. Conclusions were as follows: (1) the 11 cities' comprehensive level of the IS system shows a trend of stable increase, yet the comprehensive level of AE demonstrated a trend of fluctuation and transition. There are significant spatial variations among cities; (2) The CCD analysis results found that Hangzhou, Ningbo, and Wenzhou take the lead in realizing the transformation from barely coordinated development to superior coordinated pattern, while other cities were still in the stage of barely coordinated development; (3) the DCCD phase of 11 cities can be roughly divided into three types: upgraded-utmost development type (only Hangzhou), stable-harmonious development type (Wenzhou, Lishui, and Zhoushan) and transitional-harmonious development type (the remaining seven cities). This means, for most cities, the contradiction between the transformation process of IS and the AE has become increasingly prominent and intensified. Finally, three necessary and sustainable strategies were proposed to environmental policy makers.

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“…Thus, a synthetic evaluation method has been widely adopted in recent research [7,12,47]. In order to eliminate the deviation between subjective and objective weight, the structure entropy weight (SEW) method is introduced in this study, which considers the advantages of the subjective and objective weight methods [48][49][50]. It is a method of weight coefficient structure analysis combining qualitative analysis and quantitative analysis.…”

Section: Determination Of Indicator Weightmentioning

confidence: 99%

“…The basic principle of the SEW method involves ranking the importance degree of each index and then using the entropy method to quantitatively analyze the uncertainty of the typical order. The data of the deviation are statistically processed, and then the weight of each index is obtained [48,51]. A detailed explanation of the above steps is as follows.…”

Section: Determination Of Indicator Weightmentioning

confidence: 99%

“…The opinions of experts on typical rankings often have data noise, which may cause errors and uncertainties. The entropy theory method can effectively reduce the uncertainty of the typical rankings [48]. The specific steps are the statistical analysis and processing of the qualitative judgment conclusions of the indexes in Table 1, and the transformation equation is as follows:…”

Section: Determination Of Indicator Weightmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Development of New Urbanization from the Perspective of Coordination: A New Complex System of Urbanization‒Technology Innovation and the Atmospheric Environment

Jiang

Ding

Fang³

2019

Atmosphere

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Exploring the coordinated development of urbanization (U), technology innovation (T), and the atmospheric environment (A) is an important way to realize the sustainable development of new-type urbanization in China. Compared with existing research, we developed an integrated index system that accurately represents the overall effect of the three subsystems of UTA, and a new weight determination method, the structure entropy weight (SEW), was introduced. Then, we constructed a coordinated development index (CDI) of UTA to measure the level of sustainability of new-type urbanization. This study also analyzed trends observed in UTA for 11 cities in Zhejiang Province of China, using statistical panel data collected from 2006 to 2017. The results showed that: (1) urbanization efficiency, the benefits of technological innovation, and air quality weigh the most in the indicator systems, which indicates that they are key factors in the behavior of UTA. The subsystem scores of the 11 cities show regional differences to some extent. (2) Comparing the coordination level of UTA subsystems, we found that the order is: coordination degree of UT > coordination degree of UA > coordination degree of TA. This suggests that the atmospheric environment system improvement is an important strategic decision for sustainable urbanization in Zhejiang. (3) The UTACDI values of the 11 cities are not high enough, as the coordination is mainly low, basic, or good, while none of the cities reached the stage of excellent coordination. (4) Gray Model (1,1) revealed that the time taking to achieve excellent coordination varies for different cities. Hangzhou and Ningbo were predicted to reach the excellent coordination level in 2018. Other cities are predicted to take 2–4 years to adjust their urbanization strategies enough to be considered to have excellent coordination of their UTA system.

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Risk assessment for long-distance gas pipelines in coal mine gobs based on structure entropy weight method and multi-step backward cloud transformation algorithm based on sampling with replacement

Cited by 59 publications

References 25 publications

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

Investigating the Relationship between the Industrial Structure and Atmospheric Environment by an Integrated System: A Case Study of Zhejiang, China

Sustainable Development of New Urbanization from the Perspective of Coordination: A New Complex System of Urbanization‒Technology Innovation and the Atmospheric Environment

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