Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms
Particulate matter (PM) of sizes less than 10 µm (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow><...
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MDPI AG
2022-09-01
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Online Access: | https://www.mdpi.com/2305-6304/10/10/557 |
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author | Ahtesham Bakht Shambhavi Sharma Duckshin Park Hyunsoo Lee |
author_facet | Ahtesham Bakht Shambhavi Sharma Duckshin Park Hyunsoo Lee |
author_sort | Ahtesham Bakht |
collection | DOAJ |
description | Particulate matter (PM) of sizes less than 10 µm (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>) and 2.5 µm (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>) found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> forecasting framework is demonstrated using comparisons with the different existing deep learning models. |
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spelling | doaj.art-a77679bb6d474bf797b9af2d17e2ae892023-11-24T02:57:00ZengMDPI AGToxics2305-63042022-09-01101055710.3390/toxics10100557Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station PlatformsAhtesham Bakht0Shambhavi Sharma1Duckshin Park2Hyunsoo Lee3School of Industrial Engineering, Kumoh National Institute of Technology, Gumi 39177, KoreaTransportation System Engineering, University of Science and Technology (UST), Daejeon 34113, KoreaTransportation System Engineering, University of Science and Technology (UST), Daejeon 34113, KoreaSchool of Industrial Engineering, Kumoh National Institute of Technology, Gumi 39177, KoreaParticulate matter (PM) of sizes less than 10 µm (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula>) and 2.5 µm (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>) found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>10</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> forecasting framework is demonstrated using comparisons with the different existing deep learning models.https://www.mdpi.com/2305-6304/10/10/557particulate matterindoor subway stationdeep learninghybrid CNN-LSTMventilation control |
spellingShingle | Ahtesham Bakht Shambhavi Sharma Duckshin Park Hyunsoo Lee Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms Toxics particulate matter indoor subway station deep learning hybrid CNN-LSTM ventilation control |
title | Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms |
title_full | Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms |
title_fullStr | Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms |
title_full_unstemmed | Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms |
title_short | Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms |
title_sort | deep learning based indoor air quality forecasting framework for indoor subway station platforms |
topic | particulate matter indoor subway station deep learning hybrid CNN-LSTM ventilation control |
url | https://www.mdpi.com/2305-6304/10/10/557 |
work_keys_str_mv | AT ahteshambakht deeplearningbasedindoorairqualityforecastingframeworkforindoorsubwaystationplatforms AT shambhavisharma deeplearningbasedindoorairqualityforecastingframeworkforindoorsubwaystationplatforms AT duckshinpark deeplearningbasedindoorairqualityforecastingframeworkforindoorsubwaystationplatforms AT hyunsoolee deeplearningbasedindoorairqualityforecastingframeworkforindoorsubwaystationplatforms |