Open AccessModeling of the Safety Climate and the Cultural Attitudes to Predict Unsafe Behaviors Using the Neuro-Fuzzy Inference System (ANFIS)
Author(s) -
Reza Pourbabaki,
Zahra Beigzadeh,
Behnam Haghshenas,
Ali Karimi,
zahra alaei,
Saeid Yazdanirad
Publication year - 2020
Publication title -
archives of occupational health
Language(s) - English
Resource type - Journals
eISSN - 2588-3690
pISSN - 2588-3070
DOI - 10.18502/aoh.v4i2.2709
Subject(s) - adaptive neuro fuzzy inference system , safety behaviors , structural equation modeling , atmosphere (unit) , safety climate , matlab , inference system , computer science , descriptive statistics , statistics , psychology , fuzzy logic , occupational safety and health , machine learning , poison control , artificial intelligence , mathematics , environmental health , human factors and ergonomics , fuzzy control system , medicine , geography , pathology , operating system , meteorology
Background: Unsafe behavior in industries can be due to different factors. The aim of this study was to predict and model unsafe behavior using a safety atmosphere and cultural attitudes questionnaires. Methods: This study was a descriptive-analytic and cross-sectional examination that analyzed the data and predicted the unsafe behaviors of 90 construction workers using Neuro-Fuzzy Inference System (ANFIS) in MATLAB R2016a software. Results: In this study, the model of the safety atmosphere - unsafe behavior and the model of the cultural attitudes - unsafe behavior had the regression coefficients of 0.93373 and 0.9234, respectively. It showed that each of the parameters has a close relationship to the rate of the unsafe behavior. In this regard, a combination of the safety atmosphere and safety attitude parameters for the estimation of the unsafe behaviors achieved the better results with a regression coefficient of 0.9453 which indicates the direct effect of both parameters simultaneously on unsafe behavior. Conclusion: Based on the findings, it can be concluded that the neuro-fuzzy model can be used as an appropriate tool for predicting unsafe behavior in the industries.