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Research Papers

Evaluating the Performance and Accuracy of Incident Rate Forecasting Methods for Mining Operations

[+] Author and Article Information
Jason C. York

Department of Energy and Mineral Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: jcy127@gmail.com

Jeremy M. Gernand

Mem. ASME
Department of Energy and Mineral Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: jmg64@psu.edu

Manuscript received January 28, 2016; final manuscript received March 16, 2017; published online June 13, 2017. Assoc. Editor: Mohammad Pourgol-Mohammad.

ASME J. Risk Uncertainty Part B 3(4), 041001 (Jun 13, 2017) (16 pages) Paper No: RISK-16-1017; doi: 10.1115/1.4036309 History: Received January 28, 2016; Revised March 16, 2017

The potential benefits of a safety program are generally only realized after an incident has occurred. Resource allocation in an organization's safety program has the imperative task of balancing costs and often unrealized benefits. Management can be wary to allocate additional resources to a safety program because it is difficult to estimate the return on investment, especially since the returns are a set of negative outcomes not manifested. One way that safety professionals can provide an estimate of potential return on investment is to forecast how the organizations incident rate can be affected by implementing different resource allocation strategies and what the expected incident rate would have been without intervention. This study evaluates forecasting methods used to predict incidents against one another against a common definition of performance accuracy to identify the method that would be the most applicable to use as part of a safety resource allocation model. By identifying the most accurate forecasting method, the uncertainty of which method a safety professional should utilize for incident rate prediction is reduced. Incident data from the Mine Safety and Health Administration (MSHA) was used to make short- and long-term forecasts. The performance of each of these methods was evaluated against one another to ascertain which method has the highest level of accuracy, lowest bias, and best complexity-adjusted goodness-of-fit metrics. The double exponential smoothing and auto-regressive moving average (ARMA) statistical forecasting methods provided the most accurate incident rate predictions.

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References

Figures

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Fig. 1

Comparison of forecasted incident rate results to actual incident rates for underground mining activities 1983–2000

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Fig. 2

Comparison of forecasted incident date results to actual incident rates for surface mining activities 1983–2000

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Fig. 3

Comparison of forecasted incident rate results to actual incident rates for underground mining activities 2001–2007

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Fig. 4

Comparison of forecasted incident date results to actual incident rates for surface mining activities 2001–2007

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Fig. 5

Box and Whisker plot of the actual IR rates for underground mining 2001–2007 compared against the forecasted IR rates

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Fig. 6

Normal probability plot of the actual IRs for underground mining 2001–2007 compared against the forecasted IR for the double exponential smoothing method (α = 0.25; β = 0.25)

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Fig. 7

Box and Whisker plot of the actual IR rates for surface mining 2001–2007 compared against the forecasted IR rates

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Fig. 8

Normal probability plot of the actual IR rates for surface mining 2001–2007 compared against the forecasted IR rates for double exponential smoothing method (α = 0.25; β = 0.25)

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