Bayesian identification of oil spill source parameters from image contours
Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours o...
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Veröffentlicht in: | Marine pollution bulletin 2021-08, Vol.169, p.112514-112514, Article 112514 |
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creator | El Mohtar, Samah Ait-El-Fquih, Boujemaa Knio, Omar Lakkis, Issam Hoteit, Ibrahim |
description | Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.
•A Bayesian approach to oil spill source identification from oil contours is presented.•Full and partial images of oil slicks are used to identify the source of a spill.•Good estimates of the test spills' location, time, duration and quantity are obtained.•Confidence in the estimated source parameters is assessed through the posterior. |
doi_str_mv | 10.1016/j.marpolbul.2021.112514 |
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•A Bayesian approach to oil spill source identification from oil contours is presented.•Full and partial images of oil slicks are used to identify the source of a spill.•Good estimates of the test spills' location, time, duration and quantity are obtained.•Confidence in the estimated source parameters is assessed through the posterior.</description><identifier>ISSN: 0025-326X</identifier><identifier>EISSN: 1879-3363</identifier><identifier>DOI: 10.1016/j.marpolbul.2021.112514</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Bayesian analysis ; Bayesian estimation ; Coastal environments ; Coastal zone ; Contours ; Imagery ; Markov chain Monte Carlo ; Oil pollution ; Oil slicks ; Oil spills ; Parameter estimation ; Parameter identification ; Parameters ; Pollution sources ; Probability theory ; Remote sensing ; Remotely sensed imagery ; Satellite imagery ; Slicks ; Source identification ; Spaceborne remote sensing ; Uncertainty quantification ; Water pollution</subject><ispartof>Marine pollution bulletin, 2021-08, Vol.169, p.112514-112514, Article 112514</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright Elsevier BV Aug 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-a6906d8fb961f13fd1e9f3856b37118d47e3f525ac4a6706bea8acb2d69493f73</citedby><cites>FETCH-LOGICAL-c376t-a6906d8fb961f13fd1e9f3856b37118d47e3f525ac4a6706bea8acb2d69493f73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0025326X21005488$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>El Mohtar, Samah</creatorcontrib><creatorcontrib>Ait-El-Fquih, Boujemaa</creatorcontrib><creatorcontrib>Knio, Omar</creatorcontrib><creatorcontrib>Lakkis, Issam</creatorcontrib><creatorcontrib>Hoteit, Ibrahim</creatorcontrib><title>Bayesian identification of oil spill source parameters from image contours</title><title>Marine pollution bulletin</title><description>Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.
•A Bayesian approach to oil spill source identification from oil contours is presented.•Full and partial images of oil slicks are used to identify the source of a spill.•Good estimates of the test spills' location, time, duration and quantity are obtained.•Confidence in the estimated source parameters is assessed through the posterior.</description><subject>Bayesian analysis</subject><subject>Bayesian estimation</subject><subject>Coastal environments</subject><subject>Coastal zone</subject><subject>Contours</subject><subject>Imagery</subject><subject>Markov chain Monte Carlo</subject><subject>Oil pollution</subject><subject>Oil slicks</subject><subject>Oil spills</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Parameters</subject><subject>Pollution sources</subject><subject>Probability theory</subject><subject>Remote sensing</subject><subject>Remotely sensed imagery</subject><subject>Satellite imagery</subject><subject>Slicks</subject><subject>Source identification</subject><subject>Spaceborne remote sensing</subject><subject>Uncertainty quantification</subject><subject>Water pollution</subject><issn>0025-326X</issn><issn>1879-3363</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqFkEtLxDAUhYMoOI7-Bgtu3LTm0Sbpchx8MuBGwV1I0xvJ0DY16Qjz781QceHGzb2L-53DPQehS4ILggm_2Ra9DqPvml1XUExJQQitSHmEFkSKOmeMs2O0wJhWOaP8_RSdxbjFGAsqyAI93-o9RKeHzLUwTM46oyfnh8zbzLsui6Pr0vS7YCAbddA9TBBiZoPvM9frD8iMH6Z0j-foxOouwsXPXqK3-7vX9WO-eXl4Wq82uWGCT7nmNeattE3NiSXMtgRqy2TFGyYIkW0pgNmKVtqUmgvMG9BSm4a2vC5rZgVbouvZdwz-cwdxUr2LBrpOD-B3UdGKSVzKSuCEXv1Bt-nTIX2XKF4SwaTkiRIzZYKPMYBVY0jRwl4RrA4dq6367VgdOlZzx0m5mpWQ8n45CCoaB4OB1gUwk2q9-9fjG7FSiYc</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>El Mohtar, Samah</creator><creator>Ait-El-Fquih, Boujemaa</creator><creator>Knio, Omar</creator><creator>Lakkis, Issam</creator><creator>Hoteit, Ibrahim</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7T7</scope><scope>7TN</scope><scope>7TV</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>202108</creationdate><title>Bayesian identification of oil spill source parameters from image contours</title><author>El Mohtar, Samah ; Ait-El-Fquih, Boujemaa ; Knio, Omar ; Lakkis, Issam ; Hoteit, Ibrahim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-a6906d8fb961f13fd1e9f3856b37118d47e3f525ac4a6706bea8acb2d69493f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayesian analysis</topic><topic>Bayesian estimation</topic><topic>Coastal environments</topic><topic>Coastal zone</topic><topic>Contours</topic><topic>Imagery</topic><topic>Markov chain Monte Carlo</topic><topic>Oil pollution</topic><topic>Oil slicks</topic><topic>Oil spills</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><topic>Parameters</topic><topic>Pollution sources</topic><topic>Probability theory</topic><topic>Remote sensing</topic><topic>Remotely sensed imagery</topic><topic>Satellite imagery</topic><topic>Slicks</topic><topic>Source identification</topic><topic>Spaceborne remote sensing</topic><topic>Uncertainty quantification</topic><topic>Water pollution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>El Mohtar, Samah</creatorcontrib><creatorcontrib>Ait-El-Fquih, Boujemaa</creatorcontrib><creatorcontrib>Knio, Omar</creatorcontrib><creatorcontrib>Lakkis, Issam</creatorcontrib><creatorcontrib>Hoteit, Ibrahim</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Oceanic Abstracts</collection><collection>Pollution Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Marine pollution bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>El Mohtar, Samah</au><au>Ait-El-Fquih, Boujemaa</au><au>Knio, Omar</au><au>Lakkis, Issam</au><au>Hoteit, Ibrahim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian identification of oil spill source parameters from image contours</atitle><jtitle>Marine pollution bulletin</jtitle><date>2021-08</date><risdate>2021</risdate><volume>169</volume><spage>112514</spage><epage>112514</epage><pages>112514-112514</pages><artnum>112514</artnum><issn>0025-326X</issn><eissn>1879-3363</eissn><abstract>Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.
•A Bayesian approach to oil spill source identification from oil contours is presented.•Full and partial images of oil slicks are used to identify the source of a spill.•Good estimates of the test spills' location, time, duration and quantity are obtained.•Confidence in the estimated source parameters is assessed through the posterior.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.marpolbul.2021.112514</doi><tpages>1</tpages></addata></record> |
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subjects | Bayesian analysis Bayesian estimation Coastal environments Coastal zone Contours Imagery Markov chain Monte Carlo Oil pollution Oil slicks Oil spills Parameter estimation Parameter identification Parameters Pollution sources Probability theory Remote sensing Remotely sensed imagery Satellite imagery Slicks Source identification Spaceborne remote sensing Uncertainty quantification Water pollution |
title | Bayesian identification of oil spill source parameters from image contours |
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