PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 11m, namely PMlO. Statistical modellings are required to predi...
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2013
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my.usm.eprints.52316 http://eprints.usm.my/52316/ PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models Mohamad Japeri, Ahmad Zia Ul-Saufie TD878-894 Special types of environment. Including soil pollution, air pollution, noise pollution Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 11m, namely PMlO. Statistical modellings are required to predict future PMlO concentrations. The aims of this study are to develop and predict future PMlO concentration for next day (D+ 1), next two-days (D+2) and next three days (D+3) in seven selected monitoring stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010. 2013-07 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/52316/1/Ahmad%20Zia%20Ul-Saufie.pdf%20cut.pdf Mohamad Japeri, Ahmad Zia Ul-Saufie (2013) PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models. PhD thesis, Universiti Sains Malaysia. |
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| language |
English |
| topic |
TD878-894 Special types of environment. Including soil pollution, air pollution, noise pollution |
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TD878-894 Special types of environment. Including soil pollution, air pollution, noise pollution Mohamad Japeri, Ahmad Zia Ul-Saufie PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models |
| description |
Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research
focused on particulate matter with aerodynamic diameter less than 10 11m, namely PMlO. Statistical modellings are required to predict future PMlO concentrations. The
aims of this study are to develop and predict future PMlO concentration for next day (D+ 1), next two-days (D+2) and next three days (D+3) in seven selected monitoring
stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010. |
| format |
Thesis |
| author |
Mohamad Japeri, Ahmad Zia Ul-Saufie |
| author_facet |
Mohamad Japeri, Ahmad Zia Ul-Saufie |
| author_sort |
Mohamad Japeri, Ahmad Zia Ul-Saufie |
| title |
PM10 Concentrations Short Term Prediction
Using Regression, Artificial Neural Network And Hybrid Models |
| title_short |
PM10 Concentrations Short Term Prediction
Using Regression, Artificial Neural Network And Hybrid Models |
| title_full |
PM10 Concentrations Short Term Prediction
Using Regression, Artificial Neural Network And Hybrid Models |
| title_fullStr |
PM10 Concentrations Short Term Prediction
Using Regression, Artificial Neural Network And Hybrid Models |
| title_full_unstemmed |
PM10 Concentrations Short Term Prediction
Using Regression, Artificial Neural Network And Hybrid Models |
| title_sort |
pm10 concentrations short term prediction
using regression, artificial neural network and hybrid models |
| publishDate |
2013 |
| url |
http://eprints.usm.my/52316/1/Ahmad%20Zia%20Ul-Saufie.pdf%20cut.pdf http://eprints.usm.my/52316/ |
| _version_ |
1731226349986643968 |
| score |
13.160551 |