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dc.creatorJakariya, Md.
dc.creatorAlam, Sajadul
dc.creatorRahman, Abir
dc.creatorAhmed, Silvia
dc.creatorElahi, Lutfe
dc.creatorShabbir Khan, Abu Mohammad
dc.creatorSaad, Saman
dc.creatorTamim, H.M.
dc.creatorIshtiak, Taoseef
dc.creatorMohammad Sayem, Sheikh
dc.creatorShawkat Ali, Mirza
dc.creatorAkter, Dilruba
dc.date.accessioned2020-08-03T16:58:47Z
dc.date.available2020-08-03T16:58:47Z
dc.date.created2020
dc.identifier.issn0048-9697spa
dc.identifier.otherhttps://doi.org/10.1016/j.scitotenv.2020.140255spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/11545
dc.description.abstractThe agricultural arena in the coastal regions of South-East Asian countries is experiencing the mounting pressures of the adverse effects of climate change. Controlling and predicting climatic factors are difficult and require expensive solutions. The study focuses on identifying issues other than climatic factors using the Livelihood Vulnerability Index (LVI) to measure agricultural vulnerability. Factors such as monthly savings of the farmers, income opportunities, damage to cultivable lands, and water availability had significant impacts on increasing community vulnerability with regards to agricultural practice. The study also identified the need for assessing vulnerability after certain intervals, specifically owing to the dynamic nature of the coastal region where the factors were found to vary among the different study areas. The development of a climate-resilient livelihood vulnerability assessment tool to detect the most significant factors to assess agricultural vulnerability was done using machine learning (ML) techniques. The ML techniques identified nine significant factors out of 21 based on the minimum level of standard deviation (0.03). A practical application of the outcome of the study was the development of a mobile application. Custom REST APIs (application programming interface) were developed on the backend to seamlessly sync the app to a server, thus ensuring the acquisition of future data without much effort and resources. The paper provides a methodology for a unique vulnerability assessment technique using a mobile application, which can be used for the planning and management of resources by different stakeholders in a sustainable wayspa
dc.format.extent11 páginasspa
dc.format.mimetypeimage/jepgspa
dc.publisherScience of the Total Environmenteng
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectCoastal livelihoodspa
dc.subjectLivelihood vulnerability indexspa
dc.subjectGeographic information systemspa
dc.subjectRegression analysisspa
dc.subjectMobile applicationspa
dc.titleAssessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniquesspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.identifier.doihttps://doi.org/10.1016/j.scitotenv.2020.140255spa


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