title: A damage assessment model of oil spill accident combining historical data and satellite remote sensing information: a case study in Penglai 19-3 oil spill accident of China.
abstract: Oil spills are one of the major sources of marine pollution; it is important to conduct comprehensive assessment of losses that occur as a result of these events. Traditional methods are required to assess the three parts of losses including cleanup, socioeconomic losses, and environmental costs. It is relatively slow because assessment is complex and time consuming. A relatively quick method was developed to improve the efficiency of assessment, and then applied to the Penglai 19-3 accident. This paper uses an SAR image to calculate the oil spill area through Neural Network Classification, and uses historical oil-spill data to build the relationship between loss and other factors including sea-surface wind speed, and distance to the coast. A multiple regression equation was used to assess oil spill damage as a function of the independent variables. Results of this study can be used for regulating and quickly dealing with oil spill assessment.
authors.affiliation: College of Resources Environment and Tourism, Capital Normal University, Beijing, China; Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing, China; Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, China.
authors.forename: Lai
authors.initials: L
authors.lastname: Wei
authors.affiliation: College of Resources Environment and Tourism, Capital Normal University, Beijing, China; Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing, China; Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, China; State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Ministry of Science and Technology, Beijing, China. Electronic address: huzhuowei@mail.cnu.edu.cn.
authors.forename: Zhuowei
authors.initials: Z
authors.lastname: Hu
authors.affiliation: College of Resources Environment and Tourism, Capital Normal University, Beijing, China; Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing, China; Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Dong
authors.affiliation: College of Resources Environment and Tourism, Capital Normal University, Beijing, China; Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing, China; Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, China; State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Ministry of Science and Technology, Beijing, China.
authors.forename: Wenji
authors.initials: W
authors.lastname: Zhao
mesh_terms: D000059:Accidents; D002681:China; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods; D004787:Environmental Pollution / Q000032:analysis; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010578:Petroleum; D059392:Petroleum Pollution; D058998:Remote Sensing Technology; D014919:Wind
chemical_list: D010578:Petroleum
keywords: Comprehensive assessment; Information extraction; Loss of oil spill; Multiple regression analysis; Remote sensing