Artificial Neural Network for Predicting Accident Prevention Behavior at Work in Automotive Production Process: A Case Study in Phra Nakhon Si Ayutthaya, Thailand
Keywords:
Artificial Neural Network, Accident prevention behavior, Automotive productionAbstract
The risk of accidents at work causes injury, death, disability, chronic illness, as well as direct and indirect economic losses, including property damage due to accidents. The purpose of this cross-sectional study aimed to discover influential factors and create an accident prevention behavior prediction model among 272 workers in automotive production process during February to March 2024 in Phra Nakhon Si Ayutthaya province, Thailand. Multiple regression analysis was performed to find out the influential factors with accident prevention behavior. Artificial Neural Network ( ANN) was then used for predicting accident prevention behavior. Only four factors were significantly related to accident prevention behavior. These influential factors included.
perceived barriers to accident prevention behavior (scores), sound pressure level (dBA), heat index (oC), and accident risk perception (scores). ANN model was constructed as 4- 3- 2- 1 by comprising of 4 input variables, 3 and 2 hidden nodes, 1 output variable, momentum was 0.05, learning rate was 0.1, and learning time was 100,000 epochs. This multilayer perceptron of ANN model exhibited the least Mean Square Error (MSE). The Mean Absolute Percentage Error (MAPE) of ANN model was 3.30 percent, which indicates that the ANN model is accurate and can be used to predict individual accident prevention behavior scores in order to plan solving problems according to the influential factors with behavior scores before starting to work.
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