Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece

Grivas, Georgios; Chaloulakou, A.

doi:10.1016/j.atmosenv.2005.10.036

Cited by 289 publications

(206 citation statements)

References 31 publications

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“…Perez and Trier (2001) showed that Artificial Neural Network (ANN) model performed better than multiple-linear regression model for prediction of the nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentration (Perez et al, 2011). These results were confirmed by Grivas and Chaloulakou (2006) where PM10 was predicted using multi-layer perceptron (MLP) ANN fed by all data as input at all stations with the index of agreement between 0.8-0.89 (Grivas et al, 2006). Antanasijević * Corresponding author et al (2013) used ANN model fed by the attributes selected through genetic algorithm for PM10 prediction.…”

Section: -Introductionsupporting

confidence: 61%

“…The number of input and output neurons of the artificial neural network is determined by the nature of the problem. The performance of the MLP depends on its architecture and parameters which are derived from training, and its activation functions (Grivas et al, 2006). In this study a three layer MLP comprising of input, hidden and output layers was used.…”

Section: -2-modelingmentioning

confidence: 99%

See 1 more Smart Citation

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

Shamsoddini

Aboodi

Karami

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

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Section: -Introductionsupporting

confidence: 61%

Section: -2-modelingmentioning

confidence: 99%

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

Shamsoddini

Aboodi

Karami

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It shows the social economic activities are complex [32], multi-variable, mutual interaction [33], and directly or indirectly have great roles on air pollution [34][35][36]. Another unexpected discovery is that the indicators with less effect on PM 2.5 , have high influence in PM 10 , SO 2 and, NOx, and vice versa.…”

Section: Resultsmentioning

confidence: 99%

Influence of Social-economic Activities on Air Pollutants in Beijing, China

Zheng

Yin

et al. 2017

Open Geosciences

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Abstract:With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM 2.5 ), particulate matter with size 10 micrometers or less (PM 10 ), sulfur dioxide (SO 2 ) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM 2.5 , while PM 10 , SO 2 , and NOx present in general moderate correlations with most of the socioeconomic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have

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“…The research direction in the field tends develop tools for modeling the distribution of air pollution in near future. Gryvas and Chaloulakou (2006) tried to predict PM10 hourly concentration using neural networks in four major stations in Athens [3]. Cecchetti et al (2004) have done the same research in Milan Italy using Artificial Neural Network [4] Bruelli et al (2007) proposed a twoday ahead prediction with concentration on five particles in Palermo Italy [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network

Esfandani

Nematzadeh

2016

JAIDM

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Suspended particles have deleterious effects on human health and one of the reasons why Tehran is effected is its geographically location of air pollution. One of the most important ways to reduce air pollution is to predict the concentration of pollutants. This paper proposed a hybrid method to predict the air pollution in Tehran based on particulate matter less than 10 microns (PM10), and the information and data of Aghdasiyeh Weather Quality Control Station and Mehrabad Weather Station from 2007 to 2013. Generally, 11 inputs have been inserted to the model, to predict the daily concentration of PM10. For this purpose, Artificial Neural Network with Back Propagation (BP) with a middle layer and sigmoid activation function and its hybrid with Genetic Algorithm (BP-GA) were used and ultimately the performance of the proposed method was compared with basic Artificial Neural Networks along with (BP) Based on the criteria of -R 2 -, RMSE and MAE. The finding shows that BP-GA 2 = 0.54889 has higher accuracy and performance. In addition, it was also found that the results are more accurate for shorter time periods and this is because the large fluctuation of data in long-term returns negative effect on network performance. Also, unregistered data have negative effect on predictions. Microsoft Excel and Matlab 2013 conducted the simulations.

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Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece

Cited by 289 publications

References 31 publications

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

Influence of Social-economic Activities on Air Pollutants in Beijing, China

Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network

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