Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks

Loy-Benitez, Jorge; Vilela, Paulina; Li, Qian; Yoo, ChangKyoo

doi:10.1016/j.ecoenv.2018.11.024

Cited by 65 publications

(21 citation statements)

References 28 publications

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“…Seven studies used measured input parameters as training parameters [4,21,66,113,119,134] and five studies considered statistical analysis of features to ensure that most relevant features are fed to the network [2,75,77,132,133]. One study [66] provided a clear analysis of the relevance of features and how their in-clusion or exclusion affect the performance of the IAQ prediction system.…”

Section: Answer To Rq2mentioning

confidence: 99%

“…Moreover, the authors of [132] only provide a comparison between their methods and do not quantitatively describe metrics for their performance. Out of all these studies, four papers [2,33,77,119] provided the detailed performance analysis of the predicted hours. Note that authors in [2] used the classification method to identify good and bad air quality.…”

Section: Answer To Rq4mentioning

confidence: 99%

“…These systems can help occupants to take adequate decisions about ventilation and follow preventive measures to avoid serious health complications. By considering the advantage of [2,33,77,119] for prediction hour analysis to alert the occupants, re- [33,49,66,77,113,119,133] searchers also need to make efforts to develop a costeffective, reliable and accurate future alert-based IAQ prediction system.…”

Section: Answer To Rq4mentioning

confidence: 99%

“…The prediction systems can provide updates about future conditions of IAQ levels, and the smart building management can be adjusted accordingly to prevent serious health consequences for the building occupants. As can be seen in Table 5, four studies [2,33,77,119] provided information about the number of predicted hours using their proposed method. One study [33] claimed prediction for the next 24 hours.…”

Section: Answer To Rq6mentioning

confidence: 99%

“…One study [33] claimed prediction for the next 24 hours. However, the authors of [77] proposed prediction for 6, 12, 18 and 24 hours. Furthermore, the authors of [2] provided a prediction efficiency of 30 minutes and one hour only.…”

Section: Answer To Rq6mentioning

confidence: 99%

See 4 more Smart Citations

Indoor air quality prediction systems for smart environments: A systematic review

Saini

Dutta

Marques

2020

AIS

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Air quality is a critical matter of concern in terms of the impact on public health and well-being. Although the consequences of poor air quality are more severe in developing countries, they also have a critical impact in developed countries. Healthcare costs due to air pollution reach $150 billion in the USA, whereas particulate matter causes 412,000 premature deaths in Europe, every year. According to the Environmental Protection Agency (EPA), indoor air pollutant levels can be up to 100 times higher in comparison to outdoor air quality. Indoor air quality (IAQ) is in the top five environmental risks to global health and well-being. The research community explored the scope of artificial intelligence (AI) in the past years to deal with this problem. The IAQ prediction systems contribute to smart environments where advanced sensing technologies can create healthy living conditions for building occupants. This paper reviews the applications and potential of AI for the prediction of IAQ to enhance building environment and public health. The results show that most of the studies analyzed incorporate neural networks-based models and the preferred evaluation metrics are RMSE, R 2 score and error rate. Furthermore, 66.6% of the studies include CO2 sensors for IAQ assessment. Temperature and humidity parameters are also included in 90.47% and 85.71% of the proposed methods, respectively. This study also presents some limitations of the current research activities associated with the evaluation of the impact of different pollutants based on different geographical conditions and living environments. Moreover, the use of reliable and calibrated sensor networks for real-time data collection is also a significant challenge.

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