Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Liu, Hai-Bo; Yang, Dong; Wang, Fuzhong

doi:10.1155/2020/8863425

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…e entropy weight method [12][13][14] is an objective method, which can determine the weight of the evaluation index objectively. It is according to the difference degree of the evaluation index value, and it can avoid the deviation caused by the human factors.…”

Section: Entropy Weight Methods To Determine the Objectivementioning

confidence: 99%

Dynamic Evaluation Method of the EW-AHP Attribute Identification Model for the Tunnel Gushing Water Disaster under Interval Conditions and Applications

Chen

Huang

et al. 2021

Mathematical Problems in Engineering

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The gushing water disaster in tunnels is a kind of harmful and risky engineering disaster. It has become a key problem to evaluate the risk of tunnel gushing water accurately and objectively. A case study of a typical highway tunnel is performed for theory and practice analysis. For this reason, the risk identification is carried out on the assessed objects, and 10 evaluation indexes are determined. In turn, the risk evaluation index system and classification standard are established. Furthermore, the entropy weight method and the analytic hierarchy process are combined to assign the weight to each evaluation index. Therefore, a dynamic risk assessment system, including the pre-evaluation model and the postevaluation model, is constructed with the attribute identification model. As a result, the tunnel section with a high risk of water inrush is accurately assessed, which is consistent with the construction situation on site. Moreover, it is verified that the assessment results are reliable, which can provide a reference for the similar projects.

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Section: Entropy Weight Methods To Determine the Objectivementioning

confidence: 99%

Dynamic Evaluation Method of the EW-AHP Attribute Identification Model for the Tunnel Gushing Water Disaster under Interval Conditions and Applications

Chen

Huang

et al. 2021

Mathematical Problems in Engineering

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“…Therefore, the multi-indicator analysis method should be used to comprehensively judge the various losses of data flow after being attacked. The gray relational analysis method 16 is adopted to reduce the loss caused by information asymmetry. The specific calculation process is as follows:…”

Section: Security Risk Assessment Of Data Flow For Cellular Pathology Image Storage Systemmentioning

confidence: 99%

Secure data stream transmission method for cell pathological image storage system

Zhang

Zhao

Yin

et al. 2021

Int J of Intelligent Sys

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“…Liu et al [14] combined a gradient boosting decision tree with the KNN algorithm and proposed an improved classifier model with high prediction accuracy. Support vector machines have evident advantages in predicting small-sample gas outbursts, but they can easily fall into local optima; however, scholars have used the adaptive particle swarm algorithm and Boruta method to optimize it and achieved good prediction results [15][16][17]. Ru et al [18] first proposed the use of correlation coefficients to fill missing data and exception problems, which effectively solved the data problem and provided a new path for similar coal-gas outburst prediction problems.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

Yang

Fang

Wang

et al. 2022

IJERPH

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Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.

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Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Cited by 10 publications

References 16 publications

Dynamic Evaluation Method of the EW-AHP Attribute Identification Model for the Tunnel Gushing Water Disaster under Interval Conditions and Applications

Dynamic Evaluation Method of the EW-AHP Attribute Identification Model for the Tunnel Gushing Water Disaster under Interval Conditions and Applications

Secure data stream transmission method for cell pathological image storage system

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

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