Prediction of Indoor Air Quality Using Artificial Neural Networks

Xie, Hui; Ma, Fei; Bai, Qingyuan

doi:10.1109/icnc.2009.502

Cited by 38 publications

(26 citation statements)

References 15 publications

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“…A total of 17,885 data sets were used in this analysis. For developing the ANN model, the data were divided into three sets: 60% of the data for the whole training set (10,731 data), follows with 20% of the whole data for testing, and validation set (3577 data) respectively [8].…”

Section: Artificial Neural Network (Ann)-api Prediction Modelmentioning

confidence: 99%

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Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

Azid¹,

Juahir²,

Latif³

et al. 2013

JEP

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This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O 3 , PM 10 , NO 2 , CH 4 , NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

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Section: Artificial Neural Network (Ann)-api Prediction Modelmentioning

confidence: 99%

“…Symptoms such as eye and skin irritation, nose, throat, headache, fatigue, dizziness, and difficulty in breathing are general of health effect experienced by human due to poor air quality [8]. Worldwide, there are more deaths from indigent air quality than from automobile accident [9].…”

Section: Introductionmentioning

confidence: 99%

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

Azid¹,

Juahir²,

Latif³

et al. 2013

JEP

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“…They use a dynamic Bayesian network able to use conjointly expert knowledge and dataset. Other authors try to use the IAQ to predict the sick building syndrome [22]. They develop an artificial neural network model (ANN) able to link different surveyed pollutants, including CO2, pm, VOC, airborne bacteria and fungi, to an occupant symptom metric.…”

Section: State Of the Artmentioning

confidence: 99%

Relearning procedure to adapt pollutant prediction neural model: Choice of relearning algorithm

Thomas

Suhner

Derigent

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Predict the indoor air quality becomes a global public health issue. That's why Airbox lab® company develops a smart connected object able to measure different physical parameters including concentration of pollutants (volatile organic compounds, carbon dioxide and fine particles). This smart object must embed prediction capacities in order to avoid the exceedance of an air quality threshold. This task is performed by neural network models. However, when some events occur (change of people's behaviors, change of place of the smart connected object as example), the embedded neural models become less accurate. So a relearning step is needed in order to refit the models. This relearning must be performed by the smart connected object, and therefore, it must use the less computing time as possible. To do that, this paper propose to combine a control chart in order to limit the frequency of relearning, and to compare three learning algorithms (backpropagation, Levenberg-Marquardt, neural network with random weights) in order to choose the more adapted to this situation.

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“…Here, Artificial Neural Network Model is used for forecasting air pollution. Actions can be taken beforehand to minimize loss [13], [14].…”

Section: B Forecasting Of Air Pollutionmentioning

confidence: 99%

Smart Indoor Air Quality Monitoring System

2019

IJRTE

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 Abstract: Internet of Thing or IoT in its simplest form means "Connect the Unconnected". It makes our surroundings smarter and reactive as IoT enabled systems are capable of taking actions without human intervention. It has entered in almost all areas of life and has applications in various domains. Environment monitoring is one such domain. The air we breathe today is a mixture of harmful pollutants in high concentration. Not only outdoors, even indoors are not safe. This paper presents various IoT enabled Indoor Air Quality monitoring systems. It discusses what is indoor pollution, how it is degrading our health and how IoT is helping us to remain safe in indoors. It also stresses on the questions why indoor air pollution is to be addressed. A. Motivation for Carrying out the Analysis The need to discover presence of air pollution in our routine life (indoors and outdoors) both.  The need to understand air pollution and its impact on human cognition.  To understand existing IoT enabled indoor air monitoring applications. Hence, the problem occurring by indoor air pollution needs to be addressed and solutions in the form of IoT technology should be determined.

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Prediction of Indoor Air Quality Using Artificial Neural Networks

Cited by 38 publications

References 15 publications

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

Relearning procedure to adapt pollutant prediction neural model: Choice of relearning algorithm

Smart Indoor Air Quality Monitoring System

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