Prediction of fatal accidents in Indian factories based on ARIMA

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The inherent benefits of an accident prevention program are generally known only after an accident has occurred. The purpose of implementation of the program is to minimize the number of accidents and cost of damages. Allocation of resources to implement accident prevention program is vital because it is difficult to estimate the extent of damage caused by an accident. Accurate fatal accident predictions can provide a meaningful data that can be used to implement accident prevention program in order to minimize the cost of accidents. This paper forecast the fatal accidents of factories in India by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Accident data for the available period 1980 to 2013 was collected from the Labour bureau, Government of India to analyze the long term forecasts. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (0, 0, 1) is suitable model for prediction of fatal injuries. The number of fatal accidents is forecasted for the period 2014 to 2019. These results suggest that the policy makers and the Indian labour ministry must focus attention toward increasing fatal accidents and try to find out the reasons. It is also an opportunity for the policy makers to develop policies which may help in minimizing the number fatal accidents.

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  • Abbas M. Balkhyour A. 2015. A retrospective study about the trend analysis of industrial accidents in Pakistan International Journal of Occupational Safety and Health 5 1 – 5.

  • Al-Mutairi A. Haight J. 2009. Predicting Incident Rates: Artificial intelligence as a forecasting tool Professional Safety 54 (9) 40-48.

  • Antonio M. Tenreiro J. 2015. Power Law Behavior and Self-Similarity in Modern Industrial Accidents International Journal of Bifurcation and Chaos 25 1-12.

  • Box G. Jenkins G. 1976. Series Analysis: Forecasting and Control 4th ed. Holden - Day San Francisco.

  • Box G. Jenkins G. Reinsel G. 2008. Time Series Analysis 4th ed. John Wiley & Sons Inc Hoboken NJ.

  • Box G. Jenkins G. Reinsel G. Ljung G. 2015. Time series analysis: forecasting and control John Wiley & Sons

  • Byung W. Yun Sung L. Jung Hoon K. Rana Muhammad A. 2017. Trend Analysis of Construction Industrial Accidents in Korea from 2011 to 2015 Sustainability 9 3-12.

  • Chang W. 2014. A literature review of wind forecasting methods Journal of Power and Energy Engineering 2(2) 161-168.

  • Colak B. Etiler N. Bicer U. 2004. Fatal occupational injuries in the construction sector in Kocaeli Turkey 1990-2001 Ind. Health 42 424–430.

  • Gajbhiye P. Waghmare A. 2016. Formulation of Artificial Neural Network Model for Correlating the Factors Responsible for Industrial Accidents with Severity of Accidents Man Days Lost by using MATLAB International Journal for Innovative Research in Science & Technology 3 110-122.

  • GOI 2013. Labour Bureau Ministry of Labour & 1-176.

  • Hamalainen P. Saarela K. Takala J. 2009. Global trend according to estimated number of occupational accidents and fatal work-related diseases at region and country level Journal of safety research 40(2) 125-39.

  • Iyer P. Haight J. Del Castllo T. 2005. A research model: Forecasting incident rates for optimized safety program intervention strategies Journal of Safety Research 36(4) 341-351.

  • Oyewole S. 2009. The Implementation of statistical and Forecasting Techniques in the Assessment of Safety Intervention effectiveness and Optimization of Resource Allocation Doctorial Dissertation. The Pennsylvania State University.

  • Saad Mohd S. Fatimah S. Zairihan A. 2012. The determinants of industrial accidents in the Malaysian manufacturing sector African Journal of Business Management 6(5) 1999-2006.

  • Shumway R. Stoffer D. 2011. ARIMA models’ Time Series Analysis and Its Applications Springer 83–171

  • York J. Germand J. 2017. Evaluating the Performance and Accuracy of Incident Rate Forecasting Methods for Mining Operations ASME J. Risk Uncertainty Part B. 3(4) 1-16.

  • Zhang G. Patuwo B. Hu M. 1998. Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting 14(1) 35-62.

  • Zhang G. 2003. Time series forecasting using a hybrid ARIMA and neural network model Neuro computing 50 159-175.

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