Reference evapotranspiration time series forecasting with ensemble of convolutional neural networks

Author:

de Oliveira e Lucas PatríciaORCID,Alves Marcos AntonioORCID,de Lima e Silva Petrônio CândidoORCID,Guimarães Frederico GadelhaORCID

Funder

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference48 articles.

1. Adhikari, R., Verma, G. 2016. Time series forecasting through a dynamic weighted ensemble approach. In: 3rd International Conference on Advanced Computing, Networking and Informatics, Smart Innovation, Systems and Technologies (ICACNI). Springer India, Orissa, India, pp. 455–465. doi:10.1007/978-81-322-2538-6_47. http://link.springer.com/10.1007/978-81-322-2538-6_47.

2. Evapotranspiración del cultivo. Guías para la determinación de los requerimientos de agua de los cultivos. Technical report;Allen,2006

3. Ensemble methods for time series forecasting;Allende,2017

4. Köppen’s climate classification map for Brazil;Alvares;Meteorol. Z.,2014

5. Wavelet-multivariate relevance vector machine hybrid model for forecasting daily evapotranspiration;Bachour;Stoch. Env. Res. Risk Assess.,2016

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