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Article

Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration

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Department of Mathematics, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
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Center for Marine Studies, Pontal do Paraná Campus, Federal University of Paraná, Beira-mar Avenue, P.O. Box 61, Pontal do Paraná 83255-976, PR, Brazil
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Department of Electric Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
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Department of Mechanical Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
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Department of Mechanical, Federal University of Technology, CNPq Fellow, 5000 Dep. Heitor Alencar Furtado Street, Curitiba 81280-340, PR, Brazil
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Author to whom correspondence should be addressed.
Academic Editors: Hsiao-Chi Chuang and Alina Barbulescu
Atmosphere 2021, 12(10), 1345; https://doi.org/10.3390/atmos12101345
Received: 17 July 2021 / Revised: 1 October 2021 / Accepted: 6 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Assessing Atmospheric Pollution and Its Impacts on the Human Health)
The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes. View Full-Text
Keywords: PM10; health risks; extreme learning machine; echo state network; neural networks PM10; health risks; extreme learning machine; echo state network; neural networks
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MDPI and ACS Style

Tadano, Y.d.S.; Bacalhau, E.T.; Casacio, L.; Puchta, E.; Pereira, T.S.; Antonini Alves, T.; Ugaya, C.M.L.; Siqueira, H.V. Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration. Atmosphere 2021, 12, 1345. https://doi.org/10.3390/atmos12101345

AMA Style

Tadano YdS, Bacalhau ET, Casacio L, Puchta E, Pereira TS, Antonini Alves T, Ugaya CML, Siqueira HV. Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration. Atmosphere. 2021; 12(10):1345. https://doi.org/10.3390/atmos12101345

Chicago/Turabian Style

Tadano, Yara d.S., Eduardo T. Bacalhau, Luciana Casacio, Erickson Puchta, Thomas S. Pereira, Thiago Antonini Alves, Cássia M.L. Ugaya, and Hugo V. Siqueira 2021. "Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration" Atmosphere 12, no. 10: 1345. https://doi.org/10.3390/atmos12101345

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