| Texto completo | |
| Autor(es): |
Ribeiro, Vitor P.
;
Cunha, Angela S. M.
;
Duarte, Sergio N.
;
Padovani, Carlos R.
;
Marques, Patricia A. A.
;
Maciel, Carlos D.
;
Balestieri, Jose Antonio P.
;
IEEE
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2022-01-01. |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente |
| Beneficiário: | Marco Henrique Terra |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 21/02464-0 - Modelagem probabilística para tomada de decisão e predição em recursos hídricos: uma aplicação em segurança alimentar |
| Beneficiário: | Ângela Silviane Moura Cunha |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 18/19150-6 - Resiliência de sistemas complexos com o uso de redes bayesianas dinâmicas: uma abordagem probabilística |
| Beneficiário: | Carlos Dias Maciel |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |