30 Kasım 2022 Kitap bölümü Açık Erişim

Okan Mert KATIPOĞLU

DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.48623/aperta.262879</identifier>
  <creators>
    <creator>
      <creatorName>Okan Mert KATIPOĞLU</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6421-6087</nameIdentifier>
      <affiliation>Erzincan Binali Yıldırım University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Evaluatıon Of The Effect Of Train-Test Split Ratıo For The Prediction Of Daily Pan Evaporation Of Çorum Province</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>PAN EVAPORATION</subject>
    <subject>ÇORUM PROVINCE</subject>
    <subject>Random Forest</subject>
    <subject>Gradient boosting machine</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-11-30</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Book section</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/262879</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.48623/aperta.262878</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by-sa">Creative Commons Attribution Share-Alike</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This study aims to estimate the evaporation data in the &amp;Ccedil;orum station. For this purpose, the effect of the training-test ratio on machine learning model performance was evaluated using daily evaporation data from the &amp;Ccedil;ankırı meteorology station. Moreover, Random Forest and GBM algorithms were used to model pan evaporation. The study&amp;rsquo;s second aim is to investigate the training test rate&amp;rsquo;s effect on pan evaporation prediction. For this purpose, the training test rates were used as 90-10%, 80-20%, 70- 30%, 60-40%, and 50-50%.&lt;br&gt;
&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
</resource>