Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention

Xie, Xiaochun; Gui, Fu; Xue, Yujingyang; Zhao, Ziqi; Chen, Ping; Lu, Binfeng; Jiang, Song

doi:10.1016/j.psep.2018.11.019

Cited by 94 publications

(32 citation statements)

References 47 publications

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“…Ensuring human security simply involves doing the right thing so as to avoid unsafe acts; however, why do numerous disastrous accidents still happen? The authors suggested that there are five main causes [10][11][12]. Firstly, as people lack safety knowledge, unsafe acts arise.…”

Section: Unsafe Acts and Sourcesmentioning

confidence: 99%

Safety is about doing the right thing

Gui

Chen

Zhao

et al. 2019

Process Safety Progress

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There have been more and more accidents in the world. Since 1919, numerous accident causation theories (models) have been proposed. However, due to the complexity of previous accident causation models, it cannot avoid accidents effectively. The liners cannot understand the logical relationship between these causations and apply them in practice. The author and the group think that one of the methods used in preventing accidents is to enable people to remember the cause of an accident (namely, human errors) and do the right thing. We offer 24Model (human-thinking, simple and easyapplying) to conduct people to do the right thing for avoiding the accidents. While using the 24Model to explicitly analyze the causes of accidents and then store them in a database in split fields, the statistical law governing accident causation can be deduced. To prevent accidents, Gui Fu and the group call for open data of accident causations for the future preventions. The accumulation and sharing of safety knowledge could promote the safety behaviors, and thus create a safety culture that safety is about doing the right thing. Keypoints: (a) The three kinds and source of unsafe acts directly triggering accidents and disasters are described. (b) The model is a simple, but comprehensive, analytical framework for integrating the findings from accidents. (c) The model can be used to analysis the causes of accidents, form a logical body of accident causes and store them in a database in split fields to enable people to remember and know them. Precis: The unsafe acts arise from the lack of cognition and assessment on hazardous situations, such situations can be improved by reinforcing the safety knowledge of the employees to identify hazards.

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Section: Unsafe Acts and Sourcesmentioning

confidence: 99%

Safety is about doing the right thing

Gui

Chen

Zhao

et al. 2019

Process Safety Progress

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“…Multisource information comprehensive prediction of outbursts or multisource information artificial intelligence prediction uses a series of representative indicator systems screened from two former prediction methods, which are then analyzed by an artificial intelligence system to predict coal and gas outbursts. Recently, scholars have adopted the comprehensive evaluation (CE) method, the gray theory prediction (GTP) method, the fuzzy logic comprehensive appraisal (FLCA) method, and the artificial neural network (ANN) method to develop outburst prediction indices. Xie et al established a new coal and gas outburst prediction model that consists of four levels and fourteen factors that combined the improved fruit fly optimization algorithm (IFOA) and the general regression neural network (GRNN) algorithm to establish an IFOA‐GRNN prediction model.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, scholars have adopted the comprehensive evaluation (CE) method, the gray theory prediction (GTP) method, the fuzzy logic comprehensive appraisal (FLCA) method, and the artificial neural network (ANN) method to develop outburst prediction indices. Xie et al established a new coal and gas outburst prediction model that consists of four levels and fourteen factors that combined the improved fruit fly optimization algorithm (IFOA) and the general regression neural network (GRNN) algorithm to establish an IFOA‐GRNN prediction model.…”

Section: Introductionmentioning

confidence: 99%

Applications of online integrated system for coal and gas outburst prediction: A case study of Xinjing Mine in Shanxi, China

Zhao

Sun

Cao

et al. 2020

Energy Science & Engineering

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In this study, an indicator model and system for coal and gas outburst prediction are identified and established based on gas geological anomalies, mining stress, and gas emission characteristics. In this regard, the coal and gas outburst prevention techniques and mine rules in China are taken into account. In addition, using this model, an online integrated system and database for coal and gas outburst prediction were built for Xinjing Mine in Shanxi Province. The application results of the prediction system indicate that the system software and hardware can guarantee early warning of coal and gas outburst. The system is continuous and stable, and the early warning results are issued in a timely and accurate manner. The accuracy of the early warning results is high and consistent with the actual risk of the mine. K E Y W O R D Scase study, coal and gas outburst, coal mine, multisource information, prediction | 1981 ZHAO et Al.

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“…Therefore, the choice of the analysis method is critical to the results [12]. Currently, accident prevention research has focused on the lack of systematic cause analysis methods and analysis tools, resulting in accident analyses being incomprehensive or unspecific, so the effect of preventing accidents needs to be strengthened [13]. In order to provide a more comprehensive and clearer analysis of accident causes, this paper provides a systemic accident causation model for effective control to improve accident prevention.…”

Section: Introductionmentioning

confidence: 99%

The Accident Path of Coal Mine Gas Explosion Based on 24Model: A Case Study of the Ruizhiyuan Gas Explosion Accident

et al. 2019

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In order to effectively prevent coal mine accidents, we selected the most serious type of accident in coal mines—gas explosions—as the research object. Based on the accident causation model (24Model), we propose an action path and analysis steps of accidents caused by different employees in the organization. A gas explosion coal mine accident was analyzed using the 24Model and the proposed action path, and 12 unsafe actions, 3 unsafe states, 4 habitual behaviors, 10 safety management systems, and 10 safety cultures were obtained. Case analysis results show that by using the 24Model and path analysis the proposed effect can help employees to clearly identify the cause of the accident, to better understand the logical relationship with the causes of the accident, improve the effectiveness of training, and effectively prevent similar accidents. The 24Model and the proposed path can be used to comprehensively analyze the reasons for and help to effectively prevent coal mine gas explosion accidents.

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Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention

Cited by 94 publications

References 47 publications

Safety is about doing the right thing

Safety is about doing the right thing

Applications of online integrated system for coal and gas outburst prediction: A case study of Xinjing Mine in Shanxi, China

The Accident Path of Coal Mine Gas Explosion Based on 24Model: A Case Study of the Ruizhiyuan Gas Explosion Accident

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