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Sustainability. 2022 May 18;14(10):6147. doi: 10.3390/su14106147 Q23.32024

In-Stream Marine Litter Collection Device Location Determination Using Bayesian Network

基于贝叶斯网络的海洋漂浮垃圾收集装置入水点选择方法研究 翻译改进

Abdullah Battawi  1, Ellie Mallon  2  3, Anthony Vedral  3, Eric Sparks  3  4, Junfeng Ma  1, Mohammad Marufuzzaman  1

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作者单位

  • 1 Industrial and System Engineering Department, Mississippi State University, Starkville, MS 39762, USA; ahb264@msstate.edu (A.B.); maruf@ise.msstate.edu (M.M.).
  • 2 Osprey Initiative, LLC, Mobile, AL 36606, USA; erm385@msstate.edu.
  • 3 Coastal Research and Extension Center, Mississippi State University, Biloxi, MS 39532, USA; anthony.vedral@msstate.edu (A.V.); eric.sparks@msstate.edu (E.S.).
  • 4 Mississippi-Alabama Sea Grant Consortium, Ocean Springs, MS 39564, USA.
  • DOI: 10.3390/su14106147 PMID: 35909455

    摘要 Ai翻译

    Increased generation of waste, production of plastics, and poor environmental stewardship has led to an increase in floating litter. Significant efforts have been dedicated to mitigating this globally relevant issue. Depending on the location of floating litter, removal methods would vary, but usually include manual cleanups by volunteers or workers, use of heavy machinery to rake or sweep litter off beaches or roads, or passive litter collection traps. In the open ocean or streams, a common passive technique is to use booms and a collection receptacle to trap floating litter. These passive traps are usually installed to intercept floating litter; however, identifying the appropriate locations for installing these collection devices is still not fully investigated. We utilized four common criteria and fifteen sub-criteria to determine the most appropriate setup location for an in-stream collection device (Litter Gitter-Osprey Initiative, LLC, Mobile, AL, USA). Bayesian Network technology was applied to analyze these criteria comprehensively. A case study composed of multiple sites across the U.S. Gulf of Mexico Coast was used to validate the proposed approach, and propagation and sensitivity analyses were used to evaluate performance. The results show that the fifteen summarized criteria combined with the Bayesian Network approach could aid location selection and have practical potential for in-stream litter collection devices in coastal areas.

    Keywords: Litter Gitter; coastal; decision network; marine debris; marine litter; prevention; site selection.

    Keywords:marine litter collection; bayesian network; in-stream device

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    期刊名:Sustainability

    缩写:SUSTAINABILITY-BASEL

    ISSN:2071-1050

    e-ISSN:

    IF/分区:3.3/Q2

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    In-Stream Marine Litter Collection Device Location Determination Using Bayesian Network