Application of Cloud Model and Bayesian Network to Piracy Risk Assessment
Piracy is a major threat to maritime safety. Assessing piracy risk is crucial to ship safety, travel security, and emergency plan preparation. In the absence of a thorough understanding of the factors and mechanisms that influence piracy, no perfect mathematical equation can be set up for such risk...
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Veröffentlicht in: | Mathematical problems in engineering 2021, Vol.2021, p.1-14 |
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description | Piracy is a major threat to maritime safety. Assessing piracy risk is crucial to ship safety, travel security, and emergency plan preparation. In the absence of a thorough understanding of the factors and mechanisms that influence piracy, no perfect mathematical equation can be set up for such risk assessment. Therefore, the major factors that influence piracy were identified to construct an indicator system for assessment. These factors were analyzed, keeping in view the overall hazard, vulnerability, and antirisk properties, and then the Bayesian network was introduced into the risk assessment model to fuse multiresource information. For some indicators, which have only qualitative information or fragmentary statistical data, the cloud model theory was adopted to realize prior probability settings of the Bayesian network and thus made up for the deficiency in parameter settings. Finally, the inherent hazard of the South China Sea was assessed, as an example for the model, and two real piracy cases were studied to validate the proposed model. The assessment model constructed here can be applied to all cases, similar to the ones studied here. |
doi_str_mv | 10.1155/2021/6610339 |
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Assessing piracy risk is crucial to ship safety, travel security, and emergency plan preparation. In the absence of a thorough understanding of the factors and mechanisms that influence piracy, no perfect mathematical equation can be set up for such risk assessment. Therefore, the major factors that influence piracy were identified to construct an indicator system for assessment. These factors were analyzed, keeping in view the overall hazard, vulnerability, and antirisk properties, and then the Bayesian network was introduced into the risk assessment model to fuse multiresource information. For some indicators, which have only qualitative information or fragmentary statistical data, the cloud model theory was adopted to realize prior probability settings of the Bayesian network and thus made up for the deficiency in parameter settings. Finally, the inherent hazard of the South China Sea was assessed, as an example for the model, and two real piracy cases were studied to validate the proposed model. The assessment model constructed here can be applied to all cases, similar to the ones studied here.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/6610339</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Bayesian analysis ; Conditional probability ; Economic conditions ; Fuzzy sets ; Game theory ; Mathematical problems ; Piracy ; Qualitative analysis ; Risk assessment ; Risk factors ; Robbery ; Safety ; Statistical analysis</subject><ispartof>Mathematical problems in engineering, 2021, Vol.2021, p.1-14</ispartof><rights>Copyright © 2021 Kefeng Liu et al.</rights><rights>Copyright © 2021 Kefeng Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c380t-68cfc2f0270cdbbf13bfdfa0d5d7005a6b190a5877152bf32a6535ce53c3e2c53</citedby><cites>FETCH-LOGICAL-c380t-68cfc2f0270cdbbf13bfdfa0d5d7005a6b190a5877152bf32a6535ce53c3e2c53</cites><orcidid>0000-0003-1134-6990</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4010,27900,27901,27902</link.rule.ids></links><search><contributor>Shi, Jian</contributor><creatorcontrib>Liu, Kefeng</creatorcontrib><creatorcontrib>Yang, Lizhi</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><title>Application of Cloud Model and Bayesian Network to Piracy Risk Assessment</title><title>Mathematical problems in engineering</title><description>Piracy is a major threat to maritime safety. Assessing piracy risk is crucial to ship safety, travel security, and emergency plan preparation. In the absence of a thorough understanding of the factors and mechanisms that influence piracy, no perfect mathematical equation can be set up for such risk assessment. Therefore, the major factors that influence piracy were identified to construct an indicator system for assessment. These factors were analyzed, keeping in view the overall hazard, vulnerability, and antirisk properties, and then the Bayesian network was introduced into the risk assessment model to fuse multiresource information. For some indicators, which have only qualitative information or fragmentary statistical data, the cloud model theory was adopted to realize prior probability settings of the Bayesian network and thus made up for the deficiency in parameter settings. Finally, the inherent hazard of the South China Sea was assessed, as an example for the model, and two real piracy cases were studied to validate the proposed model. The assessment model constructed here can be applied to all cases, similar to the ones studied here.</description><subject>Bayesian analysis</subject><subject>Conditional probability</subject><subject>Economic conditions</subject><subject>Fuzzy sets</subject><subject>Game theory</subject><subject>Mathematical problems</subject><subject>Piracy</subject><subject>Qualitative analysis</subject><subject>Risk assessment</subject><subject>Risk factors</subject><subject>Robbery</subject><subject>Safety</subject><subject>Statistical analysis</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNp90EtLAzEQwPEgCtbqzQ8Q8Khr8-hkd4-1VC3UB6LgLWTzwLTbzZpsKf32bmnPnmYOP2bgj9A1JfeUAowYYXQkBCWclydoQEHwDOg4P-13wsYZZfz7HF2ktCS9BFoM0HzStrXXqvOhwcHhaR02Br8EY2usGoMf1M4mrxr8arttiCvcBfzuo9I7_OHTCk9SsimtbdNdojOn6mSvjnOIvh5nn9PnbPH2NJ9OFpnmBekyUWinmSMsJ9pUlaO8csYpYsDkhIASFS2JgiLPKbDKcaYEcNAWuOaWaeBDdHO428bwu7Gpk8uwiU3_UrJxUVJSgih7dXdQOoaUonWyjX6t4k5SIvex5D6WPMbq-e2B__jGqK3_X_8BFGBoRA</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Liu, Kefeng</creator><creator>Yang, Lizhi</creator><creator>Li, Ming</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-1134-6990</orcidid></search><sort><creationdate>2021</creationdate><title>Application of Cloud Model and Bayesian Network to Piracy Risk Assessment</title><author>Liu, Kefeng ; Yang, Lizhi ; Li, Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-68cfc2f0270cdbbf13bfdfa0d5d7005a6b190a5877152bf32a6535ce53c3e2c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayesian analysis</topic><topic>Conditional probability</topic><topic>Economic conditions</topic><topic>Fuzzy sets</topic><topic>Game theory</topic><topic>Mathematical problems</topic><topic>Piracy</topic><topic>Qualitative analysis</topic><topic>Risk assessment</topic><topic>Risk factors</topic><topic>Robbery</topic><topic>Safety</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Kefeng</creatorcontrib><creatorcontrib>Yang, Lizhi</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Kefeng</au><au>Yang, Lizhi</au><au>Li, Ming</au><au>Shi, Jian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of Cloud Model and Bayesian Network to Piracy Risk Assessment</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>14</epage><pages>1-14</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Piracy is a major threat to maritime safety. Assessing piracy risk is crucial to ship safety, travel security, and emergency plan preparation. In the absence of a thorough understanding of the factors and mechanisms that influence piracy, no perfect mathematical equation can be set up for such risk assessment. Therefore, the major factors that influence piracy were identified to construct an indicator system for assessment. These factors were analyzed, keeping in view the overall hazard, vulnerability, and antirisk properties, and then the Bayesian network was introduced into the risk assessment model to fuse multiresource information. For some indicators, which have only qualitative information or fragmentary statistical data, the cloud model theory was adopted to realize prior probability settings of the Bayesian network and thus made up for the deficiency in parameter settings. 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subjects | Bayesian analysis Conditional probability Economic conditions Fuzzy sets Game theory Mathematical problems Piracy Qualitative analysis Risk assessment Risk factors Robbery Safety Statistical analysis |
title | Application of Cloud Model and Bayesian Network to Piracy Risk Assessment |
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