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Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen, Zaher Mundher, Al-Juboori, Anas Mahmood, Beyaztas, Ufuk, Al-Ansari, Nadhir, Chau, Kwok-Wing, Qi, Chongchong, Ali, Mumtaz, Salih, Sinan Q. and Shahid, Shamsuddin 2020, Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models, Engineering applications of computational fluid mechanics, vol. 14, no. 1, pp. 70-89, doi: 10.1080/19942060.2019.1680576.

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Title Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
Author(s) Yaseen, Zaher Mundher
Al-Juboori, Anas Mahmood
Beyaztas, Ufuk
Al-Ansari, Nadhir
Chau, Kwok-Wing
Qi, Chongchong
Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Salih, Sinan Q.
Shahid, Shamsuddin
Journal name Engineering applications of computational fluid mechanics
Volume number 14
Issue number 1
Start page 70
End page 89
Total pages 20
Publisher Taylor & Francis
Place of publication Abingdon, Eng.
Publication date 2020
ISSN 1994-2060
1997-003X
Keyword(s) Science & Technology
Technology
Engineering, Multidisciplinary
Engineering, Mechanical
Mechanics
Engineering
evaporation
predictive model
machine learning
arid and semi-arid regions
best input combination
SUPPORT VECTOR REGRESSION
WATER
SOIL
IMPLEMENTATION
INTELLIGENCE
COEFFICIENT
SIMULATION
INDEX
AREA
Language eng
DOI 10.1080/19942060.2019.1680576
Indigenous content off
Field of Research 0102 Applied Mathematics
0913 Mechanical Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30131975

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.