Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were d...

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Bibliographic Details
Main Authors: Saeid Mehdizadeh, Babak Mohammadi, Quoc Bao Pham, Zheng Duan
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Water
Subjects:
reference evapotranspiration
adaptive neuro-fuzzy inference system
bio-inspired optimization algorithm
machine learning
hydrological modeling
Online Access:https://www.mdpi.com/2073-4441/13/24/3489

Internet

https://www.mdpi.com/2073-4441/13/24/3489

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