Cargando…
Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables
Accurate ET(0) estimation is of great significance in effective agricultural water management and realizing future intelligent irrigation. This study compares the performance of five Boosting-based models, including Adaptive Boosting(ADA), Gradient Boosting Decision Tree(GBDT), Extreme Gradient Boos...
Autores principales: | Wu, Tianao, Zhang, Wei, Jiao, Xiyun, Guo, Weihua, Hamoud, Yousef Alhaj |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347040/ https://www.ncbi.nlm.nih.gov/pubmed/32598399 http://dx.doi.org/10.1371/journal.pone.0235324 |
Ejemplares similares
-
Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China
por: Luo, Jia, et al.
Publicado: (2022) -
Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes
por: Ferreira, Lucas Borges, et al.
Publicado: (2021) -
Estimation of Reference Evapotranspiration in a Semi-Arid Region of Mexico
por: Delgado-Ramírez, Gerardo, et al.
Publicado: (2023) -
Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data
por: Zhao, Jing, et al.
Publicado: (2019) -
A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter
por: Ravindran, Sowmya Mangalath, et al.
Publicado: (2021)