Analysis of Regularized Echo State Networks on the Impact of Air Pollutants on Human Health

Lilian N. Araujo (), Jônatas T. Belotti, Thiago Antonini Alves (), Yara de Souza Tadano (), Flavio Trojan () and Hugo Siqueira ()
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Lilian N. Araujo: Federal University of Technology—Parana (UTFPR)
Jônatas T. Belotti: Federal University of Technology—Parana (UTFPR)
Thiago Antonini Alves: Federal University of Technology—Parana (UTFPR)
Yara de Souza Tadano: Federal University of Technology—Parana (UTFPR)
Flavio Trojan: Federal University of Technology—Parana (UTFPR)
Hugo Siqueira: Federal University of Technology—Parana (UTFPR)

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 357-364 from Springer

Abstract: Abstract Air pollution is a subject widely studied around the world, mainly due to its impacts on human health. The assessment of air pollution impact on health is often carried out using statistical regressions. This work proposes to analyze the performance of Echo State Networks with and without regularization coefficient to predict the number of hospital admissions due to air pollution and climate variables. This procedure can help the governors to take decisions in situations of high concentrations of pollutants. The results showed that the Artificial Neural Networks with regularization coefficient reached best overall results.

Keywords: Echo State Networks; Atmospheric pollution; Health; Artificial Neural Networks (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_33

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DOI: 10.1007/978-3-030-41862-5_33

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