About: Assessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniques

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Assessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniques
Creator	Ahmed, M Khan, Shabbir Rahman, Md Ali, Alam, Khan, Saman Saad, H Ahmed, Silvia Akter, Akter, Dilruba Ali, Dilruba Ishtiak, Mohammad Ishtiak, Taoseef Jakariya, Md Lutfe Elahi, Abu Lutfe Elahi, M Mirza, Shawkat Mohammad, Abu Mohammad, Shabbir Rahman, Silvia Saad, Saman Sayem, Sayem, Mohammad Tamim, H Tamim, Taoseef
Source	Elsevier; Medline; PMC
abstract	Abstract The 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 way.
has issue date	2020-06-16(xsd:dateTime)
bibo:doi	10.1016/j.scitotenv.2020.140255
bibo:pmid	32721709
has license	els-covid
sha1sum (hex)	3ca0fd74b3b0b1df0484b2bb1100f5192598df1c
schema:url	https://doi.org/10.1016/j.scitotenv.2020.140255
resource representing a document's title	Assessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniques
has PubMed Central identifier	PMC7297150
has PubMed identifier	32721709
schema:publication	Sci Total Environ
resource representing a document's body	covid:3ca0fd74b3b0b1df0484b2bb1100f5192598df1c#body_text
is schema:about of	named entity 'machine learning' named entity 'HTTPS' named entity 'COMMUNITIES' named entity 'ASSESSING' named entity 'AGRICULTURAL' named entity 'VULNERABLE' named entity 'M.S.' named entity 'Environment' named entity 'coastal communities' named entity 'doi' named entity 'coastal communities' named entity 'Bangladesh' named entity 'machine learning techniques' named entity 'mobile application' named entity 'Bangladesh' named entity 'coastal region' named entity 'climate variability' named entity 'climate change' named entity 'Bayesian optimization' named entity 'adaptive capacity' named entity 'data collection' named entity 'GIZ' named entity 'adaptive capacity' named entity 'coastal region' named entity 'Bangladesh' named entity 'random forest' named entity 'Bangladesh' named entity 'agricultural productivity' named entity 'Wald test' named entity 'IPCC' named entity 'Climate change' named entity 'p-value' named entity 'linear regression' named entity 'vulnerability assessment' named entity 'goodness of fit' named entity 'Patuakhali' named entity 'GIZ' named entity 'coastal areas' named entity 'GoB' named entity 'agricultural production' named entity 'machine learning' named entity 'Machine Learning' named entity 'overfitted' named entity 'normal distribution' named entity 'low-lying' named entity 'Islam' named entity 'coastal areas' named entity 'market conditions' named entity 'GIZ' named entity 'machine learning' named entity 'climatic' named entity 'sustainable agricultural' named entity 'climatic conditions' named entity 'highest weight' named entity 'precipitation' named entity 'floating-point' named entity 'Bangladesh'

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