{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T21:58:36Z","timestamp":1773784716418,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["405580\/2018-5"],"award-info":[{"award-number":["405580\/2018-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Atmosphere"],"abstract":"<jats:p>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\u2019 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\u2019 performance for the hospital admissions estimation by respiratory diseases, three cities of S\u00e3o Paulo state, Brazil: Cubat\u00e3o, Campinas and S\u00e3o Paulo, are investigated. Numerical results show the standard models\u2019 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\u2019 efficiency to assist the hospital admissions management during high air pollution episodes.<\/jats:p>","DOI":"10.3390\/atmos12101345","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T09:10:49Z","timestamp":1634202649000},"page":"1345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-3419","authenticated-orcid":false,"given":"Yara de Souza","family":"Tadano","sequence":"first","affiliation":[{"name":"Department of Mathematics, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-0375","authenticated-orcid":false,"given":"Eduardo Tadeu","family":"Bacalhau","sequence":"additional","affiliation":[{"name":"Center for Marine Studies, Pontal do Paran\u00e1 Campus, Federal University of Paran\u00e1, Beira-mar Avenue, P.O. Box 61, Pontal do Paran\u00e1 83255-976, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8144-1785","authenticated-orcid":false,"given":"Luciana","family":"Casacio","sequence":"additional","affiliation":[{"name":"Center for Marine Studies, Pontal do Paran\u00e1 Campus, Federal University of Paran\u00e1, Beira-mar Avenue, P.O. Box 61, Pontal do Paran\u00e1 83255-976, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3924-7217","authenticated-orcid":false,"given":"Erickson","family":"Puchta","sequence":"additional","affiliation":[{"name":"Department of Electric Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8738-4104","authenticated-orcid":false,"given":"Thomas Siqueira","family":"Pereira","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2950-7377","authenticated-orcid":false,"given":"Thiago","family":"Antonini Alves","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2709-0643","authenticated-orcid":false,"given":"C\u00e1ssia Maria Lie","family":"Ugaya","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Federal University of Technology, CNPq Fellow, 5000 Dep. Heitor Alencar Furtado Street, Curitiba 81280-340, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1278-4602","authenticated-orcid":false,"given":"Hugo Valadares","family":"Siqueira","sequence":"additional","affiliation":[{"name":"Department of Electric Engineering, Federal University of Technology, 330 Doutor Washington Subtil Chueire Street, Ponta Grossa 84017-220, PR, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","unstructured":"WHO-World Health Organization (2018). 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