Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

Lluís Sanmiquel, Marc Bascompta, Josep M. Rossell, Hernán Francisco Anticoi and Eduard Guash
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Lluís Sanmiquel: ICL Chair in Sustainable Mining, Polytechnic University of Catalonia, 08034 Barcelona, Spain
Marc Bascompta: ICL Chair in Sustainable Mining, Polytechnic University of Catalonia, 08034 Barcelona, Spain
Josep M. Rossell: Department of Mathematics, Polytechnic University of Catalonia, 08034 Barcelona, Spain
Hernán Francisco Anticoi: Department of Mining Engineering, Industrial and ICT, Polytechnic University of Catalonia, 08034 Barcelona, Spain
Eduard Guash: Department of Mining Engineering, Industrial and ICT, Polytechnic University of Catalonia, 08034 Barcelona, Spain

IJERPH, 2018, vol. 15, issue 3, 1-11

Abstract: An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

Keywords: data mining; association rules; previous cause; type of accident; overexertion (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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