1 Ocak 2023 Konferans bildirisi Açık Erişim

Celik, Mehmet Furkan; Isik, Mustafa Serkan; Erten, Esra; Taskin, Gulsen

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/266986</identifier>
  <creators>
    <creator>
      <creatorName>Celik, Mehmet Furkan</creatorName>
      <givenName>Mehmet Furkan</givenName>
      <familyName>Celik</familyName>
      <affiliation>Istanbul Tech Univ ITU, Fac Civil Engn, TR-34469 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Isik, Mustafa Serkan</creatorName>
      <givenName>Mustafa Serkan</givenName>
      <familyName>Isik</familyName>
      <affiliation>Istanbul Tech Univ ITU, Fac Civil Engn, TR-34469 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Erten, Esra</creatorName>
      <givenName>Esra</givenName>
      <familyName>Erten</familyName>
      <affiliation>Istanbul Tech Univ ITU, Fac Civil Engn, TR-34469 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Taskin, Gulsen</creatorName>
      <givenName>Gulsen</givenName>
      <familyName>Taskin</familyName>
      <affiliation>ITU, Earthquake Engn &amp; Disaster Management Inst, TR-34469 Istanbul, Turkiye</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Informative Earth Observation Variables For Cotton Yield Prediction Using Explainable Boosting Machine</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <dates>
    <date dateType="Issued">2023-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/266986</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/IGARSS52108.2023.10282371</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Cotton, a vital crop in the global textile industry, faces challenges from climate and ecosystem changes. Accurate cotton yield prediction is crucial for the economy and environmental sustainability, and it requires a deep understanding of the complex relationship between its parameters and yield. To achieve this, a comprehensive approach integrating climatic factors, soil parameters, and biophysical parameters observed through high-resolution remote sensing satellites was employed. This study utilized a multisource dataset to develop a predictive model for cotton yield over Turkiye, allowing accurate yield estimation and understanding the impact of the Earth Observation (EO)-based yield predictors on the model. Specifically, we utilized the Explainable Boosting Machine (EBM) algorithm to model and predict cotton yield while offering insights into selecting EO predictors. Additionally, we conducted a performance evaluation of our proposed approach in comparison to popular boosting-based algorithms like eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and Light gradient boosting (Light-GBM).&lt;/p&gt;</description>
  </descriptions>
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