Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting

Autores

DOI:

https://doi.org/10.4025/actascitechnol.v42i1.48203

Palavras-chave:

forecasting; air pollutants; linear multivariate; regression models; echo state networks; extreme learning machines.

Resumo

Air pollution is a relevant issue studied worldwide, and its prediction is important for social and economic management. Linear multivariate regression models (LMR) and artificial neural networks (ANN) are widely applied to forecasting concentrations of pollutants. However, unorganized machines are scarcely used. The present investigation proposes the application of unorganized machines (echo state networks - ESN and extreme learning machines - ELM) to forecast hourly concentrations of particulate matter with the aerodynamic diameter up to 10 µm (PM10), carbon monoxide (CO), and ozone (O3) at the metropolitan region of Recife, Pernambuco, Brazil. The results were compared with multilayer perceptron neural network (MLP) and LMR. The prediction was made using or not meteorological variables (wind speed, temperature, and relative humidity) as input data. The results showed that the inclusion of these variables could increase the general performance of the models considering one step ahead forecasting horizons. Also, the ELM and the LMR achieved the best overall results.

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Publicado

2020-05-28

Como Citar

Campos, D. S., Tadano, Y. de S., Alves, T. A. ., Siqueira, H. V., & Marinho, M. H. de N. (2020). Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology, 42(1), e48203. https://doi.org/10.4025/actascitechnol.v42i1.48203

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Seção

Ciência da Computação

 

0.8
2019CiteScore
 
 
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0.8
2019CiteScore
 
 
36th percentile
Powered by  Scopus

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