Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates.

Acharki S, Raza A, Vishwakarma DK, Amharref M, Bernoussi AS, Singh SK, Al-Ansari N, Dewidar AZ, Al-Othman AA, Mattar MA

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DOI: 10.1038/s41598-024-83859-6
Published: 2025 Jan 20
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-024-83859-6,
  title = {Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates.},
  author = {Acharki S and Raza A and Vishwakarma DK and Amharref M and Bernoussi AS and Singh SK and Al-Ansari N and Dewidar AZ and Al-Othman AA and Mattar MA},
  year = {2025},
  journal = {Scientific reports},
  doi = {10.1038/s41598-024-83859-6},
  url = {https://doi.org/10.1038/s41598-024-83859-6}
}

RIS

TY  - JOUR
TI  - Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates.
AU  - Acharki S
AU  - Raza A
AU  - Vishwakarma DK
AU  - Amharref M
AU  - Bernoussi AS
AU  - Singh SK
AU  - Al-Ansari N
AU  - Dewidar AZ
AU  - Al-Othman AA
AU  - Mattar MA
PY  - 2025
JO  - Scientific reports
DO  - 10.1038/s41598-024-83859-6
UR  - https://doi.org/10.1038/s41598-024-83859-6
ER  -

APA

S, A., A, R., DK, V., M, A., AS, B., SK, S., N, A., AZ, D., AA, A., & MA, M. (2025). Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates.. Scientific reports. https://doi.org/10.1038/s41598-024-83859-6

Source records

pubmed · retrieved 2026-09-26T18:21:31.282Z
europe-pmc · retrieved 2026-09-26T18:21:31.293Z
doaj · retrieved 2026-09-26T18:21:31.289Z