Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control

Rodrigo Gabriel Linares Alzamora; Paulo Nazareno Maia Sampaio; Laura Margarita Rodríguez Peralta; Ariel Isaac Posada Barrera; Éldman de Oliveira Nunes

doi:10.1007/978-3-031-93109-3_37

Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control

Rodrigo Gabriel Linares Alzamora
, Paulo Nazareno Maia Sampaio
, Laura Margarita Rodríguez Peralta
, Ariel Isaac Posada Barrera
, Éldman de Oliveira Nunes

Facultad de Ingeniería

Producción científica: Capítulo del libro/informe/acta de congreso › Articulo (Contribución a conferencia) › revisión exhaustiva

Resumen

Air pollution, especially in enclosed spaces, poses serious health risks due to everyday activities like cooking and cleaning. Poor indoor air quality can lead to conditions such as Sick Building Syndrome (SBS), highlighting the need for advanced predictive models. Kolmogorov-Arnold Networks (KAN) provide an innovative solution for predicting pollutants such as CO2, TVOC, PM2.5, and PM10 using historical and real-time data. This study applies KANs to forecast pollution risk levels and demonstrates their potential for integration with IoT technologies to enable continuous, precise monitoring for safer indoor environments.

Idioma original	Inglés
Título de la publicación alojada	Information Technology and Systems, ICITS 2025
Editores	Alvaro Rocha, Carlos Ferrás, Hiram Calvo
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	412-421
Número de páginas	10
ISBN (versión impresa)	9783031931086
DOI	https://doi.org/10.1007/978-3-031-93109-3_37
Estado	Publicada - 2025
Evento	International Conference on Information Technology and Systems, ICITS 2025 - Mexico City, México Duración: 22 ene. 2025 → 25 ene. 2025

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	1447 LNNS
ISSN (versión impresa)	2367-3370
ISSN (versión digital)	2367-3389

Conferencia

Conferencia	International Conference on Information Technology and Systems, ICITS 2025
País/Territorio	México
Ciudad	Mexico City
Período	22/01/25 → 25/01/25

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

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10.1007/978-3-031-93109-3_37

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Linares Alzamora, R. G., Maia Sampaio, P. N., Rodríguez Peralta, L. M., Posada Barrera, A. I., & de Oliveira Nunes, É. (2025). Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control. En A. Rocha, C. Ferrás, & H. Calvo (Eds.), Information Technology and Systems, ICITS 2025 (pp. 412-421). (Lecture Notes in Networks and Systems; Vol. 1447 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-93109-3_37

Linares Alzamora, Rodrigo Gabriel ; Maia Sampaio, Paulo Nazareno ; Rodríguez Peralta, Laura Margarita et al. / Sick Building Syndrome and Indoor Air Quality : Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control. Information Technology and Systems, ICITS 2025. editor / Alvaro Rocha ; Carlos Ferrás ; Hiram Calvo. Springer Science and Business Media Deutschland GmbH, 2025. pp. 412-421 (Lecture Notes in Networks and Systems).

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abstract = "Air pollution, especially in enclosed spaces, poses serious health risks due to everyday activities like cooking and cleaning. Poor indoor air quality can lead to conditions such as Sick Building Syndrome (SBS), highlighting the need for advanced predictive models. Kolmogorov-Arnold Networks (KAN) provide an innovative solution for predicting pollutants such as CO2, TVOC, PM2.5, and PM10 using historical and real-time data. This study applies KANs to forecast pollution risk levels and demonstrates their potential for integration with IoT technologies to enable continuous, precise monitoring for safer indoor environments.",

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Linares Alzamora, RG, Maia Sampaio, PN, Rodríguez Peralta, LM, Posada Barrera, AI & de Oliveira Nunes, É 2025, Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control. En A Rocha, C Ferrás & H Calvo (eds.), Information Technology and Systems, ICITS 2025. Lecture Notes in Networks and Systems, vol. 1447 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 412-421, International Conference on Information Technology and Systems, ICITS 2025, Mexico City, México, 22/01/25. https://doi.org/10.1007/978-3-031-93109-3_37

Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control. / Linares Alzamora, Rodrigo Gabriel; Maia Sampaio, Paulo Nazareno; Rodríguez Peralta, Laura Margarita et al.
Information Technology and Systems, ICITS 2025. ed. / Alvaro Rocha; Carlos Ferrás; Hiram Calvo. Springer Science and Business Media Deutschland GmbH, 2025. p. 412-421 (Lecture Notes in Networks and Systems; Vol. 1447 LNNS).

Producción científica: Capítulo del libro/informe/acta de congreso › Articulo (Contribución a conferencia) › revisión exhaustiva

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T2 - International Conference on Information Technology and Systems, ICITS 2025

AU - Linares Alzamora, Rodrigo Gabriel

AU - Maia Sampaio, Paulo Nazareno

AU - Rodríguez Peralta, Laura Margarita

AU - Posada Barrera, Ariel Isaac

AU - de Oliveira Nunes, Éldman

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PY - 2025

Y1 - 2025

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AB - Air pollution, especially in enclosed spaces, poses serious health risks due to everyday activities like cooking and cleaning. Poor indoor air quality can lead to conditions such as Sick Building Syndrome (SBS), highlighting the need for advanced predictive models. Kolmogorov-Arnold Networks (KAN) provide an innovative solution for predicting pollutants such as CO2, TVOC, PM2.5, and PM10 using historical and real-time data. This study applies KANs to forecast pollution risk levels and demonstrates their potential for integration with IoT technologies to enable continuous, precise monitoring for safer indoor environments.

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BT - Information Technology and Systems, ICITS 2025

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Y2 - 22 January 2025 through 25 January 2025

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Linares Alzamora RG, Maia Sampaio PN, Rodríguez Peralta LM, Posada Barrera AI, de Oliveira Nunes É. Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control. En Rocha A, Ferrás C, Calvo H, editores, Information Technology and Systems, ICITS 2025. Springer Science and Business Media Deutschland GmbH. 2025. p. 412-421. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-93109-3_37

Sick Building Syndrome and Indoor Air Quality: Leveraging Kolmogorov-Arnold Networks for Predictive Pollutant Control

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