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Wang F, Wang X, Liu D, Liu H. Comprehensive safety risk evaluation of fireworks production enterprises using the frequency-based ANP and BPNN. Heliyon 2023; 9:e21724. [PMID: 38027679 PMCID: PMC10658285 DOI: 10.1016/j.heliyon.2023.e21724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/19/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
The fireworks industry has long struggled with the problem of safety. Scientific, reasonable, and operable evaluation models are prerequisites of reducing risk. Based on the data from over 100 fireworks production safety accidents in China from 2010 to 2022, two evaluation models were established from the perspective of safety risk definition. Firstly, a weight calculation derivative method, the frequency-based analytic network process (ANP), was proposed creatively. This method optimized the importance ranking index calculation process in the ANP by considering the causal frequency of risk factors in the historical accident samples, thus determining how much each indicator affects the likelihood of accidents. Secondly, utilizing the historical accident samples as the dataset, a back propagation neural network (BPNN) model was developed to extract the mathematical relationship between each risk factor and the severity of accident consequence. Finally, the frequency-based ANP and BPNN models were combined to determine the safety risk level of the fireworks production enterprises. Meanwhile, the safety evaluation research samples were used as the comparison set for empirical study with historical accident samples, involving 100 fireworks production enterprises in China evaluated from 2017 to 2020. The significance result of zero shows that there is a statistically significant difference between the likelihood evaluation results of the accident and non-accident companies. Additionally, the severity evaluation model exhibits an excellent result, revealing a classification accuracy of 98.21 %, a mean square error of 8.97 × 10-4, a percent bias of 1.24 %, and a correlation coefficient and Nash-Sutcliffe efficiency coefficient both of 0.96. The frequency-based ANP and BPNN models integrate self-learning, self-adaptive, and fuzzy information processing, obtaining more accurate and objective evaluation results. This work provides a new strategy for the promotion and application of artificial intelligence in the field of safety risk evaluation, thus offering real-time safety risk evaluation and decision support of the safety management for the enterprises.
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Affiliation(s)
- Feiyue Wang
- Institute of Disaster Prevention Science and Safety Technology, School of Civil Engineering, Central South University, Changsha, 410075, Hunan, China
| | - Xinyu Wang
- Institute of Disaster Prevention Science and Safety Technology, School of Civil Engineering, Central South University, Changsha, 410075, Hunan, China
| | - Dingli Liu
- School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China
| | - Hui Liu
- Institute of Disaster Prevention Science and Safety Technology, School of Civil Engineering, Central South University, Changsha, 410075, Hunan, China
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Nallathambi I, Savaram P, Sengan S, Alharbi M, Alshathri S, Bajaj M, Aly MH, El-Shafai W. Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094365. [PMID: 37177567 PMCID: PMC10181546 DOI: 10.3390/s23094365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023]
Abstract
In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.
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Affiliation(s)
- Indumathi Nallathambi
- Department of Computer Applications, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 600089, India
- Research Scholar, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Virudhunagar 626124, India
| | - Padmaja Savaram
- Department of Computer Science and Engineering, Keshav Memorial Institute of Technology, Hyderabad 500029, India
| | - Sudhakar Sengan
- Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, India
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi Arabia
| | - Samah Alshathri
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Mohit Bajaj
- Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
- Graphic Era Hill University, Dehradun 248002, India
- Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan
| | - Moustafa H Aly
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 51718, Egypt
| | - Walid El-Shafai
- Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
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