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

Faculty of Engineering and Architecture

Research output: Chapter in Book/Report/Conference proceeding › Paper (Conference contribution) › peer-review

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.

Original language	English
Title of host publication	Information Technology and Systems, ICITS 2025
Editors	Alvaro Rocha, Carlos Ferrás, Hiram Calvo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	412-421
Number of pages	10
ISBN (Print)	9783031931086
DOIs	https://doi.org/10.1007/978-3-031-93109-3_37
State	Published - 2025
Event	International Conference on Information Technology and Systems, ICITS 2025 - Mexico City, Mexico Duration: 22 Jan 2025 → 25 Jan 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1447 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Information Technology and Systems, ICITS 2025
Country/Territory	Mexico
City	Mexico City
Period	22/01/25 → 25/01/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Big Data
IoT
Machine learning
Monitoring
Sick buildings syndrome. Kolmogorov Arnold Networks

Access to Document

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

Cite this

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. In 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. in 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, Mexico, 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).

Research output: Chapter in Book/Report/Conference proceeding › Paper (Conference contribution) › peer-review

TY - GEN

T1 - Sick Building Syndrome and Indoor Air Quality

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

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - 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.

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.

KW - Big Data

KW - IoT

KW - Machine learning

KW - Monitoring

KW - Sick buildings syndrome. Kolmogorov Arnold Networks

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

M3 - Articulo (Contribución a conferencia)

AN - SCOPUS:105018575307

SN - 9783031931086

T3 - Lecture Notes in Networks and Systems

SP - 412

EP - 421

BT - Information Technology and Systems, ICITS 2025

A2 - Rocha, Alvaro

A2 - Ferrás, Carlos

A2 - Calvo, Hiram

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 January 2025 through 25 January 2025

ER -

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. In Rocha A, Ferrás C, Calvo H, editors, 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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