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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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2018
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| Online Access: | http://eprints.usm.my/53694/ http://eprints.usm.my/53694/1/Feature%20Selection%20And%20Model%20Prediction%20Of%20Air%20Quality%20Using%20PM2.5_Sharon%20Ding%20Tiew%20Kui_K4_2018.pdf |
| _version_ | 1848882600317288448 |
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| author | Sharon Ding, Tiew Kui |
| author_facet | Sharon Ding, Tiew Kui |
| author_sort | Sharon Ding, Tiew Kui |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T18:37:29Z |
| format | Monograph |
| id | usm-53694 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:37:29Z |
| publishDate | 2018 |
| publisher | Universiti Sains Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-536942022-07-28T09:19:04Z 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) |
| spellingShingle | T Technology TP Chemical Technology Sharon Ding, Tiew Kui Feature Selection And Model Prediction Of Air Quality Using PM2.5 |
| title | 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_short | Feature Selection And Model Prediction Of Air Quality Using PM2.5 |
| title_sort | feature selection and model prediction of air quality using pm2.5 |
| topic | T Technology TP Chemical Technology |
| url | http://eprints.usm.my/53694/ http://eprints.usm.my/53694/1/Feature%20Selection%20And%20Model%20Prediction%20Of%20Air%20Quality%20Using%20PM2.5_Sharon%20Ding%20Tiew%20Kui_K4_2018.pdf |