Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM<sub>10</sub> Concentration
Yara de Souza Tadano,
Eduardo Tadeu Bacalhau,
Luciana Casacio,
Erickson Puchta,
Thomas Siqueira Pereira,
Thiago Antonini Alves,
Cássia Maria Lie Ugaya,
Hugo Valadares Siqueira
Affiliations
Yara de Souza Tadano
Department of Mathematics, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
Eduardo Tadeu Bacalhau
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
Luciana Casacio
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
Erickson Puchta
Department of Electric Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
Thomas Siqueira Pereira
Department of Mechanical Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
Thiago Antonini Alves
Department of Mechanical Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
Cássia Maria Lie Ugaya
Department of Mechanical, Federal University of Technology, CNPq Fellow, 5000 Dep. Heitor Alencar Furtado Street, Curitiba 81280-340, PR, Brazil
Hugo Valadares Siqueira
Department of Electric Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil
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.
