Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction

Willian De Assis Pedrobon Ferreira; Ian Grout; Alexandre Cesar Rodrigues Da Silva

doi:10.1109/iEECON53204.2022.9741563

Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction

Willian De Assis Pedrobon Ferreira
, Ian Grout
, Alexandre Cesar Rodrigues Da Silva

Department of Electronics & Computer Engineering

Universidade Estadual Paulista Júlio de Mesquita Filho

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

Abstract

Indoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.

Original language	English
Title of host publication	Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665402064
DOIs	https://doi.org/10.1109/iEECON53204.2022.9741563
Publication status	Published - 2022
Event	2022 International Electrical Engineering Congress, iEECON 2022 - Khon Kaen, Thailand Duration: 9 Mar 2022 → 11 Mar 2022

Publication series

Name	Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022

Conference

Conference	2022 International Electrical Engineering Congress, iEECON 2022
Country/Territory	Thailand
City	Khon Kaen
Period	9/03/22 → 11/03/22

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

fuzzy ARTMAP neural network
indoor air quality
online training
particulate matter prediction

Access to Document

10.1109/iEECON53204.2022.9741563

Cite this

De Assis Pedrobon Ferreira, W., Grout, I., & Da Silva, A. C. R. (2022). Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. In Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022 (Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iEECON53204.2022.9741563

De Assis Pedrobon Ferreira, Willian ; Grout, Ian ; Da Silva, Alexandre Cesar Rodrigues. / Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022).

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title = "Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction",

abstract = "Indoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.",

keywords = "fuzzy ARTMAP neural network, indoor air quality, online training, particulate matter prediction",

author = "\{De Assis Pedrobon Ferreira\}, Willian and Ian Grout and \{Da Silva\}, \{Alexandre Cesar Rodrigues\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Electrical Engineering Congress, iEECON 2022 ; Conference date: 09-03-2022 Through 11-03-2022",

year = "2022",

doi = "10.1109/iEECON53204.2022.9741563",

language = "English",

series = "Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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De Assis Pedrobon Ferreira, W, Grout, I & Da Silva, ACR 2022, Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. in Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022. Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Electrical Engineering Congress, iEECON 2022, Khon Kaen, Thailand, 9/03/22. https://doi.org/10.1109/iEECON53204.2022.9741563

Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. / De Assis Pedrobon Ferreira, Willian; Grout, Ian; Da Silva, Alexandre Cesar Rodrigues.
Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022).

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

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AU - Grout, Ian

AU - Da Silva, Alexandre Cesar Rodrigues

PY - 2022

Y1 - 2022

N2 - Indoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.

AB - Indoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.

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KW - indoor air quality

KW - online training

KW - particulate matter prediction

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BT - Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022

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De Assis Pedrobon Ferreira W, Grout I, Da Silva ACR. Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. In Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022). doi: 10.1109/iEECON53204.2022.9741563

Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction

Abstract

Publication series

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UN SDGs

Keywords

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