Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Indumathi Nallathambi; Padmaja Savaram; Sudhakar Sengan; Meshal Alharbi; Samah Alshathri; Mohit Bajaj; Moustafa H Aly; Walid El-Shafai

doi:10.3390/s23094365

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Sensors (Basel). 2023 Apr 28;23(9):4365. doi: 10.3390/s23094365.

Authors

Indumathi Nallathambi^{1

2}, Padmaja Savaram³, Sudhakar Sengan⁴, Meshal Alharbi⁵, Samah Alshathri⁶, Mohit Bajaj^{7

8

9}, Moustafa H Aly¹⁰, Walid El-Shafai^{11

12}

Affiliations

¹ Department of Computer Applications, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 600089, India.
² Research Scholar, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Virudhunagar 626124, India.
³ Department of Computer Science and Engineering, Keshav Memorial Institute of Technology, Hyderabad 500029, India.
⁴ Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, India.
⁵ Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi Arabia.
⁶ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁷ Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India.
⁸ Graphic Era Hill University, Dehradun 248002, India.
⁹ Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan.
¹⁰ Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 51718, Egypt.
¹¹ Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia.
¹² Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt.

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.

Keywords: environmental sustainability; fireworks industry; human disaster; human factors; machine learning; mitigation.

MeSH terms

Accidents*
Bayes Theorem
Hazardous Substances*
Humans
India
Machine Learning

Substances

Hazardous Substances

Grants and funding

This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R197), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.