A comprehensive model of vessel anchoring pressure based on machine learning to support the sustainable management of the marine environments of coastal cities
Liu, Baijing1,2; Gong, Meng3; Wu, Xiaoqing1,4; Liu, Xin1
Source PublicationSUSTAINABLE CITIES AND SOCIETY
ISSN2210-6707
2021-09-01
Volume72Pages:10
KeywordVessel anchoring pressure Automatic identification system Machine learning Illegal anchoring area Sustainable marine management
DOI10.1016/j.scs.2021.103011
Corresponding AuthorWu, Xiaoqing(xiaoqingwuyic@163.com) ; Liu, Xin(xliu@yic.ac.cn)
AbstractThe increased utilization of marine areas represents a significant challenge to the sustainable eco-environmental management of coastal cities. Machine learning, specifically the support-vector machine classification algorithm, was used to preprocess the massive Automatic identification System (AIS) dataset and extract anchoring vessels. Then, a comprehensive indicator evaluation model for anchoring pressure (CAPI) was constructed to evaluate the potential marine ecological pressure associated with anchoring vessels in the Bohai Sea. Spatial analysis was performed by geographic information system (GIS) to identify improper anchoring areas with high CAPI values. Finally, anchorage management in various coastal cities was assessed. The results showed that: (1) machine learning technology accurately identified anchoring vessels, (2) improper anchoring in the Bohai Sea is common, and (3) the management of anchoring activities is generally poor at boundaries between administrative regions. This study provides a rapid, feasible, and effective visualization method for marine environmental managers both theoretically and practically. The data mining method and CAPI model proposed here facilitate the management of vessel-related social issues in coastal cities, and they will help decision makers to quickly formulate targeted management measures to support the sustainable economic and environmental development of coastal cities.
Funding OrganizationNational Key R&D Program of China ; Shandong Provincial Natural Science Foundation
Indexed BySCI
Language英语
WOS KeywordAIS DATA ; INDICATORS ; STRESSORS ; SEAGRASS ; IMPACTS ; SCIENCE
WOS Research AreaConstruction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels
WOS IDWOS:000672607600003
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Document Type期刊论文
Identifierhttp://ir.yic.ac.cn/handle/133337/29477
Collection中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
中科院海岸带环境过程与生态修复重点实验室
Corresponding AuthorWu, Xiaoqing; Liu, Xin
Affiliation1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101400, Peoples R China
3.Prov Geomat Ctr Jiangsu, Nanjing 210013, Jiangsu, Peoples R China
4.Chinese Acad Sci, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
Recommended Citation
GB/T 7714
Liu, Baijing,Gong, Meng,Wu, Xiaoqing,et al. A comprehensive model of vessel anchoring pressure based on machine learning to support the sustainable management of the marine environments of coastal cities[J]. SUSTAINABLE CITIES AND SOCIETY,2021,72:10.
APA Liu, Baijing,Gong, Meng,Wu, Xiaoqing,&Liu, Xin.(2021).A comprehensive model of vessel anchoring pressure based on machine learning to support the sustainable management of the marine environments of coastal cities.SUSTAINABLE CITIES AND SOCIETY,72,10.
MLA Liu, Baijing,et al."A comprehensive model of vessel anchoring pressure based on machine learning to support the sustainable management of the marine environments of coastal cities".SUSTAINABLE CITIES AND SOCIETY 72(2021):10.
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