Application of structural equation modeling to evaluate coal and gas outbursts

Kursunoglu, Nilufer; Önder, Mustafa

doi:10.1016/j.tust.2019.02.017

Cited by 71 publications

(33 citation statements)

References 40 publications

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“…However, econometric regression methods cannot show the indirect and direct interrelationships that may exist among multiple variables [26]. The structural equation model (SEM) is a relatively new and comprehensive method that can deal with the relationship between multiple causes and results [27]. It is a special form of multivariate analysis that examines the roles and inter-relationships of multiple variables, which can be used to determine the relationship among farmers' risk cognition, risk preferences and climate change adaptive behavior.…”

Section: Introductionmentioning

confidence: 99%

Farmers’ Risk Cognition, Risk Preferences and Climate Change Adaptive Behavior: A Structural Equation Modeling Approach

Jia

Kuang

et al. 2019

IJERPH

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Improving local farmers climate change adaptive capacity is an important policy issue in rural China. This study investigates farmers risk cognition, risk preferences and climate change adaptive behavior. Based on unique data from a survey and a paired lottery experiment completed by 240 rural farmers in Chongqing City of China, this paper finds that farmers have a pessimistic risk cognition towards climate change and the typical farmers are risk-averse and loss-averse. Risk cognition and adaptation cognition have significantly positive influences on climate change adaptive behavior, and loss aversion has a significantly positive influence on farmers adaptation decisions. Loss aversion exerts a positive impact on risk cognition and adaptation cognition, and risk aversion has a positive impact on adaptation cognition. This paper contributes to the emerging literature that relates risk preference in experiments and risk cognition to farmers climate change adaptive behavior.

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Section: Introductionmentioning

confidence: 99%

Farmers’ Risk Cognition, Risk Preferences and Climate Change Adaptive Behavior: A Structural Equation Modeling Approach

Jia

Kuang

et al. 2019

IJERPH

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“…Based on the Unipore Crank Diffusion Model for spherical grains and plane sheets, Norbert et al [9] analyzed the phenomenon of gas emission during a coal and gas outburst. Using a statistical method, Nilufer et al [10] employed Structural Equation Modeling (SEM) to identify the relationship between the parameters (mining depth, coal seam gas content, and moisture content) and contributing factors (seam thickness, seam inclination, and distance from fault) affecting the coal and gas outbursts for an underground coal mine in Turkey and to analyze multiple interactions among them. They found that mining depth is the most significant factor among all factors, and distance from fault is the most significant contributing factor to outburst.…”

Section: Of 13mentioning

confidence: 99%

Expansionary Evolution Characteristics of Plastic Zone in Rock and Coal Mass Ahead of Excavation Face and the Mechanism of Coal and Gas Outburst

2020

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Factors leading to coal and gas outbursts are complicated, and the accuracy of predicting outbursts remains difficult to achieve. Therefore, coal and gas outburst risk are still a scientific challenge in the fields of mining, geology, safe engineering, and rock mechanics. Theories explaining and predicting coal and gas outbursts associated with restored strain and gas energy are hot topics. Based on numerical modelling, the evolution of a plastic zone ahead of an excavation face with change of direction and magnitude of regional pre-mining stress field were analyzed. A mechanical analysis model for the expansionary evolution process of the plastic zone ahead of the excavation face was constructed, and the expansionary evolution characteristics of plastic zone ahead of the excavation face were analyzed. The physical and mechanical process and basic conditions for the initiation and development of coal and gas outbursts induced by evolution of plastic zone were discussed. The research provides some new insights into the mechanism, prediction, and prevention of goal and gas outbursts.

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“…Recent years has seen much attention being paid to prediction and prewarning of gas disaster. A variety of relevant methods have been developed at scholars at home and abroad [5][6][7][8][9][10]. An et al [5] established a set of prewarning criteria for electromagnetic (EM) radiation of gas outburst, after analyzing multiple factors: the features and laws of EM radiation in coal rock damages, as well as the change laws of and correlations between EM radiation and common prewarning indices in gas outburst.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on the Hadoop platform, Hao and Zhang [6] puts forward a gas outburst prediction and prewarning method in the following steps: the real-time monitored data of gas volume fraction were preprocessed through Holt's exponential smoothing, the characteristic parameters of the preprocessed data were extracted by backpropagation neural network (BPNN), and then the gas outburst prediction and prewarning model was established, coupling the parameters of manual outburst detection. Kursunoglu and Onder [7] screened the prewarning indies for gas outburst, determined the weight of each prewarning index through the analytic hierarchy process (AHP), and constructed a gas outburst prewarning model based on extension theory. Li et al [8] set up an intelligent discriminant model for gas outburst hazard, which integrates advanced techniques and theories like artificial neural network (ANN), multi-factor pattern recognition, and the theories on mine pressure and gas drainage.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fuzzy Fusion Algorithm of Multi-sensor Data and Its Application in Coalmine Gas Monitoring

Zhu¹,

Jiang²,

Zhao³

et al. 2019

I2M

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In coalmines, gas disaster is a complex uncertain process influenced by multiple factors. It is difficult to predict gas disaster in an accurate manner. The traditional methods for gas disaster prediction face several common problems, such as the inaccuracy of gas monitoring data, and the unreliable evaluation of gas safety. To solve the problems, this paper proposes a gas monitoring and prewarning method based on fuzzy fusion of multi-sensor data. To realize data fusion and decision-making, the field parameters like wind speed, gas content and temperature, which are monitored by multiple sensors in the coalmine, were allocated to the set of factors, while the decisions on gas state were allocated to the set of remarks. Then, the data collected by the sensors were fuzzified by the fuzzy set theory, creating a fuzzy membership matrix. The fuzzy Cauchy-Riemann equations were introduced to establish the membership function. Furthermore, the coalmine gas state was evaluated and determined in the fusion center under decision-making rules through compositional operation. Based on local decisions, a data fusion decision-making model for coalmine gas disaster was established to make the global decision. The proposed method was applied to analyze the temperature, gas content and wind speed of tunnel face monitored by multiple sensors at three different time points in a coalmine of Shanxi Province, China. The results show that the gas states at all time points were evaluated accurately, without any false or missed alarm, and the prediction based on multi-sensor data fusion was 34% more accurate than that based on single-sensor data. The research findings provide an effective way to monitor and prewarn the coalmine gas state.

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Application of structural equation modeling to evaluate coal and gas outbursts

Cited by 71 publications

References 40 publications

Farmers’ Risk Cognition, Risk Preferences and Climate Change Adaptive Behavior: A Structural Equation Modeling Approach

Farmers’ Risk Cognition, Risk Preferences and Climate Change Adaptive Behavior: A Structural Equation Modeling Approach

Expansionary Evolution Characteristics of Plastic Zone in Rock and Coal Mass Ahead of Excavation Face and the Mechanism of Coal and Gas Outburst

A Novel Fuzzy Fusion Algorithm of Multi-sensor Data and Its Application in Coalmine Gas Monitoring

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