A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting
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Springer
Abstract
Description
Industrial forecasting is a top-echelon research
domain, which has over the past several years experienced
highly provocative research discussions. The scope of this
research domain continues to expand due to the continuous
knowledge ignition motivated by scholars in the area. So,
more intelligent and intellectual contributions on current
research issues in the accident domain will potentially spark
more lively academic, value-added discussions that will be of
practical significance to members of the safety community. In
this communication, a new grey–fuzzy–Markov time series
model, developed from nondifferential grey interval analytical
framework has been presented for the first time. This
instrument forecasts future accident occurrences under timeinvariance
assumption. The actual contribution made in the
article is to recognise accident occurrence patterns and
decompose theminto grey state principal pattern components.
The architectural framework of the developed grey–fuzzy–
Markov pattern recognition (GFMAPR) model has four
stages: fuzzification, smoothening, defuzzification and
whitenisation. The results of application of the developed
novel model signify that forecasting could be effectively
carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting
investigations. The novelty of thework lies in the capability of
the model inmaking highly accurate predictions and forecasts
based on the availability of small or incomplete accident data.
Keywords
T Technology (General), TJ Mechanical engineering and machinery