Data triangulation and machine learning: a hybrid approach to fill missing climate data

Author:

Lima Vinícius Haender C.,de Arruda Pereira MarconiORCID

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Publisher

Springer Science and Business Media LLC

Reference39 articles.

1. Alvares CA, Stape JL, Sentelhas PC et al (2013) Köppen’s climate classification map for Brazil. Meteorol Z 22(6):711–728

2. Alves A, Kobiyama M, Silva RV, et al (2006) Análise de dados hidrológicos na região do município de alfredo wagner/sc. simpósio de Recursos Hídricos do sul 1

3. Alvisi S, Franchini M (2011) Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environ Model Softw 26(4):523–537

4. Azim S, Aggarwal S (2014) Hybrid model for data imputation: using fuzzy c means and multi layer perceptron. In: 2014 IEEE international advance computing conference (IACC). IEEE, pp 1281–1285

5. Bayma LO, Pereira MA (2017) Comparison of machine learning techniques for the estimation of climate missing data in the state of Minas Gerais, Brazil. Proceeding of XVII Brazilian Symposium of Geoinformatics

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