Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities

Tariq, Shahzeb; Loy-Benitez, Jorge; Nam, KiJeon; Lee, Gahye; Kim, Minjeong; Park, DuckShin

doi:10.1016/j.jhazmat.2020.124753

Cited by 35 publications

(8 citation statements)

References 53 publications

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“…In addition, the development of an algorithmic framework can help reduce data dependency. For instance, Tariq et al [130] developed a framework for predicting pollutant concentrations in subway stations by combining transfer learning (TL) and neural networks. The study involved building a pre-training model with a well-measured subway station dataset and subsequently fine-tuning it with lesser data from other subway stations, ultimately providing well-performing prediction models for four subway stations.…”

Section: Modelling Processmentioning

confidence: 99%

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Dong

Goodman

Rajagopalan

2023

IJERPH

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Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures. Methods: This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion. Results: After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models. Conclusions: This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.

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Section: Modelling Processmentioning

confidence: 99%

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Dong

Goodman

Rajagopalan

2023

IJERPH

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“…CNN and other deep learning models are widely used in real-time air quality modeling [ 44 ]. Shahzeb et al [ 3 ] used a residual neural network (Resnet-50)-based modified version to predict

concentration in a newly built subway station. Its input data consisted of 5 input attributes and 12 past observations.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…While it offers a convenient way of transportation, its internal air quality raises concern. If not properly ventilated, it causes nitrogen dioxide, carbon dioxide, carbon monoxide, and particulate matter to accumulate over time [ 3 ]. Particulate matter (PM) and pollutants such as sulfur dioxide (

), nitrogen oxides (

), carbon monoxide ( CO ), and others that are present in the air above a certain threshold are known to cause several health problems, such as non-malignant respiratory disease, asthma, and allergies; a higher mortality rate; and early death [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Bakht

Sharma

Park

et al. 2022

Toxics

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Particulate matter (PM) of sizes less than 10 µm () and 2.5 µm () 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 and 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 and forecasting framework is demonstrated using comparisons with the different existing deep learning models.

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“…TL is widely used in the domains of image classification (Dai et al, 2007 ), speech and natural language processing (Bel et al, 2003 ; Blitzer et al, 2006 ; Ling et al, 2008 ), building utilization (Arief-Ang et al, 2018 ), and neurophysiological studies (Atyabi et al, 2013 ; Tu & Sun, 2012 ). To enhance the forecast accuracy, TL has also been used in atmospheric research (Tariq et al, 2021 ). It is widely used in prediction of air pollutants, especially in case of scarcity of data, where transferring the knowledge learned on the pre-trained model improves the prediction accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

Dutta

Pal

2022

Environ Monit Assess

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The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018–23 March 2020), complete lockdown (24 March 2020–31 May 2020), and partial lockdown (1 June 2020–30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM 10 and PM 2.5 ). However, the effect of the lockdown is most prominent on PM 2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM 2.5 and PM 10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM 2.5 and PM 10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May–15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96–120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

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Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities

Cited by 35 publications

References 53 publications

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

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