Data/hora: 31/05/2024 06:03:42

Biblioteca(s): 	Embrapa Pantanal.	
Data corrente: 	16/11/2022	
Data da última atualização: 	17/01/2023	
Autoria: 	RIBEIRO, V. P.; PADOVANI, C. R.; BALESTIERI, J. A. P. B.; CUNHA, A. S. M.; MARQUES, P. A. A.; DUARTE, S. N.; MACIEL, C. D.	
Afiliação: 	VITOR P. RIBEIRO, Universidade Estadual Paulista; CARLOS ROBERTO PADOVANI, CPAP; JOSE ANTONIO P. BALESTIERI, Universidade Estadual Paulista; ANGELA S. M. CUNHA, Universidade de São Paulo; PATRICIA A. A. MARQUES, Universidade de São Paulo; SERGIO N. DUARTE, Universidade de São Paulo; CARLOS D. MACIEL, Universidade de São Paulo.	
Título: 	Bayesian network for hydrological model: an inference approach.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022, Padua, Italy, Proceedings...[S.l.: s.n.], 2022.	
DOI: 	10.1109/IJCNN55064.2022.9892468	
Idioma: 	Português	
Conteúdo: 	Abstract: According to the Food and Agriculture Organisation, there are growing concerns about the availability and use of water in agriculture. The hydrological model generates a water balance and the resulting value indicates the amount of available water in a given area. The calculation of the water balance is fundamental for the development of new strategies for the management of water resources. One of its main adversities is the estimation of evapotranspiration, which may be considered a fundamental component. This factor considers climatological variables collected from weather stations that are spread over large areas. However, there are frequent cases of long periods of missing data. We evaluated the performance of a Bayesian Network inference model for estimating evapotranspiration in a large agricultural region in Brazil. To this end, the method considered factors such as accuracy, missing data, and model portability. The results indicate that the model achieves up to 86% accuracy when comparing estimated values to expected values derived from the Penman-Monteith equation. The results show that wind speed and relative humidity are the most critical climatological variables for accurate estimation.	
Thesagro: 	Balanço Hídrico; Evapotranspiração.	
Thesaurus Nal: 	Bayesian theory; Evapotranspiration; Water balance.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Pantanal (CPAP)	
Exemplares: 	CPAP - (60796-1) - UPC - AA - DD -  -  -  - DIS