Predicting the occurrence of natural and technological disasters in Greece through Verhulst, multinomial and exponential models.
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| Title: | Predicting the occurrence of natural and technological disasters in Greece through Verhulst, multinomial and exponential models. |
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| Authors: | Mavrakis, Anastasios1 (AUTHOR) mavrakisan@yahoo.gr, Lykoudis, Spyridon2 (AUTHOR), Salvati, Luca3 (AUTHOR) |
| Source: | Safety Science. Oct2023, Vol. 166, pN.PAG-N.PAG. 1p. |
| Subjects: | Natural disasters, Emergency management, Communities, Time perspective, Databases |
| Geographic Terms: | Greece |
| Abstract: | • This is a novel framework for estimating the number of future disasters in Greece. • The approach is used in population and mixed biometric-econometric studies. • This information is important for policy makers and planning authorities. • The approach improved the preparedness of risk management plans. A rising number and variety of hazards has threatened local communities, possibly leading to disasters that are cause of important (human and economic) losses across regions and countries. As a consequence, both preparedness and management of hazard risk emerged as relevant issues in advanced economies. The present study proposes an original framework to estimate the number of future disasters in Greece using the Emergency Database (EM-DAT) as primary data. This information is vital to adopt precaution measures and design official plans for the protection of resident population. For this purpose, a logistic model based on Verhulst equations was tested here – using multinomial and exponential models as computational alternatives – on a complete database considering disasters occurred in Greece between 1904 and 2020. The outcomes of all models have documented the increasing frequency of all kinds of disasters over time. Predictions covering a time horizon that includes the 2020 s estimated the occurrence of 1 to 9 disasters per year as a whole (1 to 6 events per year for natural disasters and 1 to 3 events per year for technological disasters). These findings justify the urgent need of effective policies improving preparedness of local communities. [ABSTRACT FROM AUTHOR] |
| Copyright of Safety Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 165468853 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting the occurrence of natural and technological disasters in Greece through Verhulst, multinomial and exponential models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mavrakis%2C+Anastasios%22">Mavrakis, Anastasios</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mavrakisan@yahoo.gr</i><br /><searchLink fieldCode="AR" term="%22Lykoudis%2C+Spyridon%22">Lykoudis, Spyridon</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Salvati%2C+Luca%22">Salvati, Luca</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Safety+Science%22">Safety Science</searchLink>. Oct2023, Vol. 166, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Natural+disasters%22">Natural disasters</searchLink><br /><searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Communities%22">Communities</searchLink><br /><searchLink fieldCode="DE" term="%22Time+perspective%22">Time perspective</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Greece%22">Greece</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • This is a novel framework for estimating the number of future disasters in Greece. • The approach is used in population and mixed biometric-econometric studies. • This information is important for policy makers and planning authorities. • The approach improved the preparedness of risk management plans. A rising number and variety of hazards has threatened local communities, possibly leading to disasters that are cause of important (human and economic) losses across regions and countries. As a consequence, both preparedness and management of hazard risk emerged as relevant issues in advanced economies. The present study proposes an original framework to estimate the number of future disasters in Greece using the Emergency Database (EM-DAT) as primary data. This information is vital to adopt precaution measures and design official plans for the protection of resident population. For this purpose, a logistic model based on Verhulst equations was tested here – using multinomial and exponential models as computational alternatives – on a complete database considering disasters occurred in Greece between 1904 and 2020. The outcomes of all models have documented the increasing frequency of all kinds of disasters over time. Predictions covering a time horizon that includes the 2020 s estimated the occurrence of 1 to 9 disasters per year as a whole (1 to 6 events per year for natural disasters and 1 to 3 events per year for technological disasters). These findings justify the urgent need of effective policies improving preparedness of local communities. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Safety Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=165468853 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ssci.2023.106246 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Natural disasters Type: general – SubjectFull: Emergency management Type: general – SubjectFull: Communities Type: general – SubjectFull: Time perspective Type: general – SubjectFull: Databases Type: general – SubjectFull: Greece Type: general Titles: – TitleFull: Predicting the occurrence of natural and technological disasters in Greece through Verhulst, multinomial and exponential models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mavrakis, Anastasios – PersonEntity: Name: NameFull: Lykoudis, Spyridon – PersonEntity: Name: NameFull: Salvati, Luca IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09257535 Numbering: – Type: volume Value: 166 Titles: – TitleFull: Safety Science Type: main |
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