Study of Spanish mining accidents using data mining techniques

Mining is an economic sector with a high number of accidents. Mines are hazardous places and workers can suffer a wide variety of injuries. Utilizing a database composed of almost 70,000 occupational accidents and fatality reports corresponding to the decade 2003–2012 in the Spanish mining sector, t...

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Autores: Sanmiquel Pera, Lluís|||0000-0001-5612-4713, Rossell Garriga, Josep Maria|||0000-0002-5631-5357, Vintró Sánchez, Carla|||0000-0003-4189-1500
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/26427
Acceso en línea:https://hdl.handle.net/2117/26427
https://dx.doi.org/10.1016/j.ssci.2015.01.016
Access Level:acceso abierto
Palabra clave:Mine safety
Data mining
Mine accidents
Mining accidents
Bayesian network
Classification methods
Mines -- Mesures de seguretat
Mineria de dades
Mines -- Accidents
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria de mines
Àrees temàtiques de la UPC::Economia i organització d'empreses::Seguretat industrial::Prevenció de riscos laborals
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repository_id_str
spelling Study of Spanish mining accidents using data mining techniquesSanmiquel Pera, Lluís|||0000-0001-5612-4713Rossell Garriga, Josep Maria|||0000-0002-5631-5357Vintró Sánchez, Carla|||0000-0003-4189-1500Mine safetyData miningMine accidentsMining accidentsData miningBayesian networkClassification methodsMines -- Mesures de seguretatMineria de dadesMines -- AccidentsÀrees temàtiques de la UPC::Enginyeria civil::Enginyeria de minesÀrees temàtiques de la UPC::Economia i organització d'empreses::Seguretat industrial::Prevenció de riscos laboralsMining is an economic sector with a high number of accidents. Mines are hazardous places and workers can suffer a wide variety of injuries. Utilizing a database composed of almost 70,000 occupational accidents and fatality reports corresponding to the decade 2003–2012 in the Spanish mining sector, the paper analyzes the main causes of those accidents. To carry out the study, powerful statistical tools have been applied, such as Bayesian classi¿ers, decision trees or contingency tables, among other data mining techniques. Statistical analyses have been performed using Weka software and behavioral patterns based on certain rules have been obtained. From these rules, some conclusions are extracted which can help to develop suitable prevention policies to reduce injuries and fatalities.Peer Reviewed20152015-06-0120152015-02-19journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/26427https://dx.doi.org/10.1016/j.ssci.2015.01.016reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/264272026-05-27T15:37:01Z
dc.title.none.fl_str_mv Study of Spanish mining accidents using data mining techniques
title Study of Spanish mining accidents using data mining techniques
spellingShingle Study of Spanish mining accidents using data mining techniques
Sanmiquel Pera, Lluís|||0000-0001-5612-4713
Mine safety
Data mining
Mine accidents
Mining accidents
Data mining
Bayesian network
Classification methods
Mines -- Mesures de seguretat
Mineria de dades
Mines -- Accidents
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria de mines
Àrees temàtiques de la UPC::Economia i organització d'empreses::Seguretat industrial::Prevenció de riscos laborals
title_short Study of Spanish mining accidents using data mining techniques
title_full Study of Spanish mining accidents using data mining techniques
title_fullStr Study of Spanish mining accidents using data mining techniques
title_full_unstemmed Study of Spanish mining accidents using data mining techniques
title_sort Study of Spanish mining accidents using data mining techniques
dc.creator.none.fl_str_mv Sanmiquel Pera, Lluís|||0000-0001-5612-4713
Rossell Garriga, Josep Maria|||0000-0002-5631-5357
Vintró Sánchez, Carla|||0000-0003-4189-1500
author Sanmiquel Pera, Lluís|||0000-0001-5612-4713
author_facet Sanmiquel Pera, Lluís|||0000-0001-5612-4713
Rossell Garriga, Josep Maria|||0000-0002-5631-5357
Vintró Sánchez, Carla|||0000-0003-4189-1500
author_role author
author2 Rossell Garriga, Josep Maria|||0000-0002-5631-5357
Vintró Sánchez, Carla|||0000-0003-4189-1500
author2_role author
author
dc.subject.none.fl_str_mv Mine safety
Data mining
Mine accidents
Mining accidents
Data mining
Bayesian network
Classification methods
Mines -- Mesures de seguretat
Mineria de dades
Mines -- Accidents
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria de mines
Àrees temàtiques de la UPC::Economia i organització d'empreses::Seguretat industrial::Prevenció de riscos laborals
topic Mine safety
Data mining
Mine accidents
Mining accidents
Data mining
Bayesian network
Classification methods
Mines -- Mesures de seguretat
Mineria de dades
Mines -- Accidents
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria de mines
Àrees temàtiques de la UPC::Economia i organització d'empreses::Seguretat industrial::Prevenció de riscos laborals
description Mining is an economic sector with a high number of accidents. Mines are hazardous places and workers can suffer a wide variety of injuries. Utilizing a database composed of almost 70,000 occupational accidents and fatality reports corresponding to the decade 2003–2012 in the Spanish mining sector, the paper analyzes the main causes of those accidents. To carry out the study, powerful statistical tools have been applied, such as Bayesian classi¿ers, decision trees or contingency tables, among other data mining techniques. Statistical analyses have been performed using Weka software and behavioral patterns based on certain rules have been obtained. From these rules, some conclusions are extracted which can help to develop suitable prevention policies to reduce injuries and fatalities.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-06-01
2015
2015-02-19
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/26427
https://dx.doi.org/10.1016/j.ssci.2015.01.016
url https://hdl.handle.net/2117/26427
https://dx.doi.org/10.1016/j.ssci.2015.01.016
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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