<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Prediction of the Work-Related Injuries Based on Neural Networks

System Safety: Human - Technical Facility - Environment

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Prediction of the Work-Related Injuries Based on Neural Networks"}

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...

Prediction of the Work-Related Injuries Based on Neural Networks

Publié en ligne: 20 juin 2021

Pages: 19 - 37

Reçu: 10 déc. 2020

Accepté: 02 févr. 2021

DOI: https://doi.org/10.2478/czoto-2021-0003

Mots clés
mining industry, safety, work-related injuries, artificial neural networks

© 2021 Jelena Ivaz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Prediction of the Work-Related Injuries Based on Neural Networks

Publié en ligne: 20 juin 2021

Pages: 19 - 37

Reçu: 10 déc. 2020

Accepté: 02 févr. 2021

DOI: https://doi.org/10.2478/czoto-2021-0003

Mots clésmining industry, safety, work-related injuries, artificial neural networks

© 2021 Jelena Ivaz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
mining industry, safety, work-related injuries, artificial neural networks