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Feature Selection And Model Prediction Of Air Quality Using PM2.5

This study was to develop a feed-forward artificial neural network (FANN) prediction model to predict the air quality using PM2.5. Currently, Malaysia does not have any prediction model for concentration of PM2.5. Thus, with the prediction model developed, the concentration of PM2.5 in air can be pr...

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Bibliographic Details
Main Author: Sharon Ding, Tiew Kui
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2018
Subjects:
T Technology
TP Chemical Technology
Online Access:http://eprints.usm.my/53694/1/Feature%20Selection%20And%20Model%20Prediction%20Of%20Air%20Quality%20Using%20PM2.5_Sharon%20Ding%20Tiew%20Kui_K4_2018.pdf
http://eprints.usm.my/53694/
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spelling my.usm.eprints.53694 http://eprints.usm.my/53694/ Feature Selection And Model Prediction Of Air Quality Using PM2.5 Sharon Ding, Tiew Kui T Technology TP Chemical Technology This study was to develop a feed-forward artificial neural network (FANN) prediction model to predict the air quality using PM2.5. Currently, Malaysia does not have any prediction model for concentration of PM2.5. Thus, with the prediction model developed, the concentration of PM2.5 in air can be predicted by using meteorological variables. The main parameter that investigated in this study was the number of neuron of hidden layer. The performance of the prediction model was analysed and evaluated by using mean square error (MSE) and Coefficient of Determination (R2) values. With the increasing of the number of neuron of hidden layer, MSE decreased and R increased. 10 neuron of hidden layer gave the best performance among the number of neuron investigated. Due to the low performance of the prediction model, feature selection was introduced to remove irrelevant variables in data set. Random forest (RF) was grew with 200 regression trees to decide the importance of the predictors. The predictors which was less important were removed from the predictors. With the removal of the irrelevant variables, the precision of the prediction model increased with increased of the performance of the model. Besides that, the complexity of the prediction model also reduced by decreasing training time of the prediction model. The predictors removed by feature selection in this study were pressure, dew point, hourly precipitation and cumulated precipitation. Thus, it was clearly seen that the performance of prediction model with feature selection was better than prediction model without feature selection. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53694/1/Feature%20Selection%20And%20Model%20Prediction%20Of%20Air%20Quality%20Using%20PM2.5_Sharon%20Ding%20Tiew%20Kui_K4_2018.pdf Sharon Ding, Tiew Kui (2018) Feature Selection And Model Prediction Of Air Quality Using PM2.5. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Kimia. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TP Chemical Technology
spellingShingle T Technology
TP Chemical Technology
Sharon Ding, Tiew Kui
Feature Selection And Model Prediction Of Air Quality Using PM2.5
description This study was to develop a feed-forward artificial neural network (FANN) prediction model to predict the air quality using PM2.5. Currently, Malaysia does not have any prediction model for concentration of PM2.5. Thus, with the prediction model developed, the concentration of PM2.5 in air can be predicted by using meteorological variables. The main parameter that investigated in this study was the number of neuron of hidden layer. The performance of the prediction model was analysed and evaluated by using mean square error (MSE) and Coefficient of Determination (R2) values. With the increasing of the number of neuron of hidden layer, MSE decreased and R increased. 10 neuron of hidden layer gave the best performance among the number of neuron investigated. Due to the low performance of the prediction model, feature selection was introduced to remove irrelevant variables in data set. Random forest (RF) was grew with 200 regression trees to decide the importance of the predictors. The predictors which was less important were removed from the predictors. With the removal of the irrelevant variables, the precision of the prediction model increased with increased of the performance of the model. Besides that, the complexity of the prediction model also reduced by decreasing training time of the prediction model. The predictors removed by feature selection in this study were pressure, dew point, hourly precipitation and cumulated precipitation. Thus, it was clearly seen that the performance of prediction model with feature selection was better than prediction model without feature selection.
format Monograph
author Sharon Ding, Tiew Kui
author_facet Sharon Ding, Tiew Kui
author_sort Sharon Ding, Tiew Kui
title Feature Selection And Model Prediction Of Air Quality Using PM2.5
title_short Feature Selection And Model Prediction Of Air Quality Using PM2.5
title_full Feature Selection And Model Prediction Of Air Quality Using PM2.5
title_fullStr Feature Selection And Model Prediction Of Air Quality Using PM2.5
title_full_unstemmed Feature Selection And Model Prediction Of Air Quality Using PM2.5
title_sort feature selection and model prediction of air quality using pm2.5
publisher Universiti Sains Malaysia
publishDate 2018
url http://eprints.usm.my/53694/1/Feature%20Selection%20And%20Model%20Prediction%20Of%20Air%20Quality%20Using%20PM2.5_Sharon%20Ding%20Tiew%20Kui_K4_2018.pdf
http://eprints.usm.my/53694/
_version_ 1739829014036480000
score 13.211869

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