Prediction of Indoor Air Quality Using Artificial Neural Networks

2009 ◽  
Author(s):  
Hui Xie ◽  
Fei Ma ◽  
Qingyuan Bai
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Artificial Neural

scholarly journals Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 952
Author(s):  
He Zhang ◽  
Ravi Srinivasan ◽  
Xu Yang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Time Series Regression ◽  
Ann Models ◽  
Mlr Model

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones.

scholarly journals ARTIFICIAL NEURAL NETWORKS: AN APPLICATION IN THE STUDY OF AIR QUALITY

2014 ◽  
Vol 13 ◽  
Author(s):  
Amaury De Souza ◽  
Hamilton Germano Pavão ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Multilayer Perceptron ◽  
Artificial Neural

A estimativa da concentração do ozônio de superfície propicia a geração de dados para o planejamento de previsão da qualidade do ar, útil na gestão de saúde publica. O objetivo deste trabalho foi elaborar uma Rede Neural Artificial (RNAs) para estimar a concentração do ozônio de superfície em função de dados diários de clima. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência da concentração diária do ozônio medida. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. O desempenho da RNA desenvolvida foi muito bom, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da concentração do ozônio de superfície. O modelo proposto pode ser utilizado pelo governo público como ferramenta para ativar ações de ferramentas durante os períodos de estagnação atmosférica, quando os níveis de ozônio na atmosfera possam representar riscos à saúde publica.

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

scholarly journals Indoor Air Quality Control Using Backpropagated Neural Networks

2022 ◽  
Vol 70 (2) ◽  
pp. 3837-3853
Author(s):  
Raissa Uskenbayeva ◽  
Aigerim Altayeva ◽  
Faryda Gusmanova ◽  
Gluyssya Abdulkarimova ◽  
Saule Berkimbaeva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Control ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Quality Control
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