Application of machine learning techniques to simulate the evaporative fraction and its relationship...
Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops
About this item
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Background
The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algo...
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Full title
Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops
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TN_cdi_doaj_primary_oai_doaj_org_article_5f9f18465e5149b1b8547c5ed33f18df
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_5f9f18465e5149b1b8547c5ed33f18df
Other Identifiers
ISSN
2192-1709
E-ISSN
2192-1709
DOI
10.1186/s13717-022-00400-1
