Prediction of the work-related injuries based on neural networks

• Autorzy: Ivaz Jelena , Nikolić Ružica R. , Petrović Dejan , Djoković Jelena M. , Hadzima Branislav

Identyfikatory

DOI

10.2478/czoto-2021-0003

• Języki publikacji: EN

Abstrakty

EN

Artificial neural networks (ANN) are a powerful tool in the decision-making process, especially in solving the complex problems with a large number of input data. The possibility to predict the work-related injuries in the underground coal mines, based on application of the neural networks, is analyzed in this work. the input data for the network were obtained based on a survey of 1300 respondents. After analyzing the input data influence on the network output, 14 most influential inputs were selected, with help of which the network correctly predicted whether the worker would suffer the work-related injury or not, with 80% precision. The two models were developed, based on the multilayer perceptron (MLP) and radial basis function (RBF) networks. The two models' results were compared to each other. The sensitivity analysis was used to select the most influential parameters, like mine, age of miners, as well as their work experience. The parameters were further analyzed by use of the descriptive statistics. The selected parameters are direct indicators of problems that can cause injuries. The obtained results point to the fact that the work-related injuries can be successfully predicted by application of the artificial neural networks. The proposed models' importance is reflected in the clear indicators for enforcing the stricter occupational safety and organizational measures in order to reduce the number of work-related injuries in underground mines.

Słowa kluczowe

EN

mining industry; safety; work-related injuries; artificial neural networks

PL

przemysł górniczy; bezpieczeństwo; wypadek przy pracy; sztuczne sieci neuronowe

• Wydawca: De Gruyter
• Czasopismo: System Safety : Human - Technical Facility - Environment
• Rocznik: 2021
• Tom: Vol. 3, nr 1
• Strony: 19--37
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Ivaz Jelena

• Technical Faculty of Bor, University of Belgrade, Serbia

• https://orcid.org/0000-0001-9263-6804+

autor

Nikolić Ružica R.

• Research Center, University of Žilina, Slovakia

• https://orcid.org/0000-0003-3042-8916+

autor

Petrović Dejan

• Technical Faculty of Bor, University of Belgrade, Serbia

• https://orcid.org/0000-0001-8588-2046+

autor

Djoković Jelena M.

• Technical Faculty of Bor, University of Belgrade, Serbia

• https://orcid.org/0000-0001-5542-8088+

autor

Hadzima Branislav

• Research Center, University of Žilina, Slovakia

• https://orcid.org/0000-0002-8201-905X+

Bibliografia

• [1] Chena, H., Luoa, X., 2016. Severity Prediction Models of Falling Risk for Workers et Height. Procedia Engineering, 164, 439-445, DOI: 10.1016/j.proeng.2016.11.642.
• [2] Ciarapica, F.E., Giacchetta, G., 2009. Classification and prediction of occupational injury risk using soft computing techniques: An Italian study, Safety Science, 47(1), 36-49, DOI: 10.1016/j.ssci.2008.01.006.
• [3] Delen, D., Sharda, R., Bessonov, M., 2005. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks, Accident Analysis and Prevention, 38(3), 434-444, DOI: 10.1016/j.aap.2005.06.024.
• [4] Karra, V.K., 2005. Analysis of non-fatal and fatal injury rates for mine operator and contractor employees and the influence of work location, Journal of Safety Research 36(5), 413-421, DOI: 10.1016/j.jsr.2005.08.002.
• [5] Rivas, T., Paz, M., Martin, J.E., Matias, J.A., Garcia, J.F., Taboada, J., 2011. Explaining and predicting workplace accidents using data-mining techniques, Reliability Engineering and System Safety 96(7), 739-747, DOI: 10.1016/j.ress. 2011.03.006.
• [6] Sari, M., Duzgun, H.S.B., Karpuz, C., Selcuk, A.S., 2004. Accident analysis of two Turkish underground coal mines, Safety Science, 4298, 675-690, DOI: 10.1016/j. ssci.2003.11.002.
• [7] Sarkar, S., Vinay, S., Raj, R., Maiti, J., Mitra, P., 2019. Application of optimized machine learning techniques for prediction of occupational accidents, Computers and Operations Research, 106, 210-224, DOI: 10.1016/j.cor.2018.02.021.
• [8] Stojadinovic, S., Svrkuta, I., Petrović, D., Denic, M., Pantovic, R., Milić, V., 2012. Mining injuries in Serbian underground coal mines – A 10-year study, Injury 43(12), 2001-2005, DOI: 10.1016/j.injury.2011.08.018.
• [9] Stylianou, N., Akbarov, A., Kontopantelis, E., Buchan, I, Dunn, K.W., 2015. Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches, Burns 41(5), 925-934. DOI: 10.1016/j.burns.2015.03.016.
• [10] Vallmuur, K., 2015. Machine learning approaches to analysing textual injury surveillance data: A systematic review, Accident Analysis and Prevention, 79, 41-49. DOI: 10.1016/j.aap.2015.03.018.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).