A Stochastic Bayesian Artificial Intelligence Framework to Assess Climatological Water Balance under Missing Variables for Evapotranspiration Estimates

A Stochastic Bayesian Artificial Intelligence Framework to Assess Climatological Water Balance under Missing Variables for Evapotranspiration Estimates

The sustainable use of water resources is of utmost importance given climatological changes and water scarcity, alongside the many socioeconomic factors that rely on clean water availability, such as food security. In this context, developing tools to minimize water waste in irrigation is paramount...

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Bibliographic Details
Main Authors: Vitor P. Ribeiro, Luiz Desuó Neto, Patricia A. A. Marques, Jorge A. Achcar, Adriano M. Junqueira, Adilson W. Chinatto, Cynthia C. M. Junqueira, Carlos D. Maciel, José Antônio P. Balestieri
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Agronomy
Subjects:
irrigation planning
decision support system
artificial intelligence
stochastic modeling
Bayesian inference
Online Access:https://www.mdpi.com/2073-4395/13/12/2970

Internet

https://www.mdpi.com/2073-4395/13/12/2970

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