A risk prediction model for Maritime accidents

Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment a...
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Autor*in:	Medda, Andrea [verfasserIn] Serra, Patrizia [verfasserIn] Mandas, Marco [verfasserIn] Fancello, Gianfranco [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Maritime accidents Machine learning approaches Maritime safety Accidents prediction

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: WMU journal of maritime affairs - Springer Berlin Heidelberg, 2002, 23(2024), 3 vom: 12. Juni, Seite 415-436
Übergeordnetes Werk:	volume:23 ; year:2024 ; number:3 ; day:12 ; month:06 ; pages:415-436

Links:	Volltext

DOI / URN:	10.1007/s13437-024-00337-6

Katalog-ID:	SPR057146578

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allfields	10.1007/s13437-024-00337-6 doi (DE-627)SPR057146578 (SPR)s13437-024-00337-6-e DE-627 ger DE-627 rakwb eng 550 VZ Medda, Andrea verfasserin aut A risk prediction model for Maritime accidents 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. Maritime accidents (dpeaa)DE-He213 Machine learning approaches (dpeaa)DE-He213 Maritime safety (dpeaa)DE-He213 Accidents prediction (dpeaa)DE-He213 Serra, Patrizia verfasserin aut Mandas, Marco verfasserin aut Fancello, Gianfranco verfasserin aut Enthalten in WMU journal of maritime affairs Springer Berlin Heidelberg, 2002 23(2024), 3 vom: 12. Juni, Seite 415-436 (DE-627)642887438 (DE-600)2587034-8 1654-1642 nnns volume:23 year:2024 number:3 day:12 month:06 pages:415-436 https://dx.doi.org/10.1007/s13437-024-00337-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 23 2024 3 12 06 415-436
spelling	10.1007/s13437-024-00337-6 doi (DE-627)SPR057146578 (SPR)s13437-024-00337-6-e DE-627 ger DE-627 rakwb eng 550 VZ Medda, Andrea verfasserin aut A risk prediction model for Maritime accidents 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. Maritime accidents (dpeaa)DE-He213 Machine learning approaches (dpeaa)DE-He213 Maritime safety (dpeaa)DE-He213 Accidents prediction (dpeaa)DE-He213 Serra, Patrizia verfasserin aut Mandas, Marco verfasserin aut Fancello, Gianfranco verfasserin aut Enthalten in WMU journal of maritime affairs Springer Berlin Heidelberg, 2002 23(2024), 3 vom: 12. Juni, Seite 415-436 (DE-627)642887438 (DE-600)2587034-8 1654-1642 nnns volume:23 year:2024 number:3 day:12 month:06 pages:415-436 https://dx.doi.org/10.1007/s13437-024-00337-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 23 2024 3 12 06 415-436
allfields_unstemmed	10.1007/s13437-024-00337-6 doi (DE-627)SPR057146578 (SPR)s13437-024-00337-6-e DE-627 ger DE-627 rakwb eng 550 VZ Medda, Andrea verfasserin aut A risk prediction model for Maritime accidents 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. Maritime accidents (dpeaa)DE-He213 Machine learning approaches (dpeaa)DE-He213 Maritime safety (dpeaa)DE-He213 Accidents prediction (dpeaa)DE-He213 Serra, Patrizia verfasserin aut Mandas, Marco verfasserin aut Fancello, Gianfranco verfasserin aut Enthalten in WMU journal of maritime affairs Springer Berlin Heidelberg, 2002 23(2024), 3 vom: 12. Juni, Seite 415-436 (DE-627)642887438 (DE-600)2587034-8 1654-1642 nnns volume:23 year:2024 number:3 day:12 month:06 pages:415-436 https://dx.doi.org/10.1007/s13437-024-00337-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 23 2024 3 12 06 415-436
allfieldsGer	10.1007/s13437-024-00337-6 doi (DE-627)SPR057146578 (SPR)s13437-024-00337-6-e DE-627 ger DE-627 rakwb eng 550 VZ Medda, Andrea verfasserin aut A risk prediction model for Maritime accidents 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. Maritime accidents (dpeaa)DE-He213 Machine learning approaches (dpeaa)DE-He213 Maritime safety (dpeaa)DE-He213 Accidents prediction (dpeaa)DE-He213 Serra, Patrizia verfasserin aut Mandas, Marco verfasserin aut Fancello, Gianfranco verfasserin aut Enthalten in WMU journal of maritime affairs Springer Berlin Heidelberg, 2002 23(2024), 3 vom: 12. Juni, Seite 415-436 (DE-627)642887438 (DE-600)2587034-8 1654-1642 nnns volume:23 year:2024 number:3 day:12 month:06 pages:415-436 https://dx.doi.org/10.1007/s13437-024-00337-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 23 2024 3 12 06 415-436
allfieldsSound	10.1007/s13437-024-00337-6 doi (DE-627)SPR057146578 (SPR)s13437-024-00337-6-e DE-627 ger DE-627 rakwb eng 550 VZ Medda, Andrea verfasserin aut A risk prediction model for Maritime accidents 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. Maritime accidents (dpeaa)DE-He213 Machine learning approaches (dpeaa)DE-He213 Maritime safety (dpeaa)DE-He213 Accidents prediction (dpeaa)DE-He213 Serra, Patrizia verfasserin aut Mandas, Marco verfasserin aut Fancello, Gianfranco verfasserin aut Enthalten in WMU journal of maritime affairs Springer Berlin Heidelberg, 2002 23(2024), 3 vom: 12. Juni, Seite 415-436 (DE-627)642887438 (DE-600)2587034-8 1654-1642 nnns volume:23 year:2024 number:3 day:12 month:06 pages:415-436 https://dx.doi.org/10.1007/s13437-024-00337-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 23 2024 3 12 06 415-436
language	English
source	Enthalten in WMU journal of maritime affairs 23(2024), 3 vom: 12. Juni, Seite 415-436 volume:23 year:2024 number:3 day:12 month:06 pages:415-436
sourceStr	Enthalten in WMU journal of maritime affairs 23(2024), 3 vom: 12. Juni, Seite 415-436 volume:23 year:2024 number:3 day:12 month:06 pages:415-436
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Maritime accidents Machine learning approaches Maritime safety Accidents prediction
dewey-raw	550
isfreeaccess_bool	true
container_title	WMU journal of maritime affairs
authorswithroles_txt_mv	Medda, Andrea @@aut@@ Serra, Patrizia @@aut@@ Mandas, Marco @@aut@@ Fancello, Gianfranco @@aut@@
publishDateDaySort_date	2024-06-12T00:00:00Z
hierarchy_top_id	642887438
dewey-sort	3550
id	SPR057146578
language_de	englisch
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author	Medda, Andrea
spellingShingle	Medda, Andrea ddc 550 misc Maritime accidents misc Machine learning approaches misc Maritime safety misc Accidents prediction A risk prediction model for Maritime accidents
authorStr	Medda, Andrea
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topic_title	550 VZ A risk prediction model for Maritime accidents Maritime accidents (dpeaa)DE-He213 Machine learning approaches (dpeaa)DE-He213 Maritime safety (dpeaa)DE-He213 Accidents prediction (dpeaa)DE-He213
topic	ddc 550 misc Maritime accidents misc Machine learning approaches misc Maritime safety misc Accidents prediction
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title	A risk prediction model for Maritime accidents
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title_full	A risk prediction model for Maritime accidents
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title_sort	a risk prediction model for maritime accidents
title_auth	A risk prediction model for Maritime accidents
abstract	Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. © The Author(s) 2024
abstractGer	Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. © The Author(s) 2024
abstract_unstemmed	Abstract This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence. © The Author(s) 2024
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container_issue	3
title_short	A risk prediction model for Maritime accidents
url	https://dx.doi.org/10.1007/s13437-024-00337-6
remote_bool	true
author2	Serra, Patrizia Mandas, Marco Fancello, Gianfranco
author2Str	Serra, Patrizia Mandas, Marco Fancello, Gianfranco
ppnlink	642887438
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doi_str	10.1007/s13437-024-00337-6
up_date	2024-08-30T04:49:56.238Z
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code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2024</subfield><subfield code="e">3</subfield><subfield code="b">12</subfield><subfield code="c">06</subfield><subfield code="h">415-436</subfield></datafield></record></collection>
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