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

Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimati...

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Автори:	Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, Mina Amharref, Abdes Samed Bernoussi, Sudhir Kumar Singh, Nadhir Al-Ansari, Ahmed Z. Dewidar, Ahmed A. Al-Othman, Mohamed A. Mattar
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Nature Portfolio 2025-01-01
Серія:	Scientific Reports
Предмети:	Reference evapotranspiration Light gradient boosting machine Hybrid model FAO-56 Penman-Monteith model Subhumid and semi-arid zones
Онлайн доступ:	https://doi.org/10.1038/s41598-024-83859-6
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