Zailan, Masezatti (2018) Prediction Of PM10 Concentration Using Multiple Linear Regression And Support Vector Machine. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Awam. (Submitted)
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Abstract
Particulate matter with an aerodynamic diameter less than 10µm (PM10) is one of the most air pollutants that can give negative effect on human health and environment. The purpose of this research is to predict the particulate matter concentration for the next day (PM10D1) by using Multiple Linear Regression (MLR) and Support Vector Machine (SVM) models. The meteorological and gaseous parameters that are used in this study are particulate matter for today (PM10D0), wind speed (WS), temperature (TEMP), relative humidity (RH), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO). The daily mean data that are used in this study are divided into training data (70%) and validation data (30%) and are used from 2013 until 2015. Four monitoring stations were selected in this study to predict the PM10 concentration for the next day (PM10D1) which are Jerantut which act as background station, Nilai (industrial area), Seberang Jaya (sub-urban area) and Shah Alam (urban area). The results of overall data that are obtained from this study has shown that Nilai monitoring stations contributed the highest mean value of PM10 concentration compared to the other monitoring stations. This indicated that Nilai is a more polluted area as it is known as a highly industrialised area. The results shows that Multiple Linear Regression (MLR) is the best model in predicting PM10 concentration for the next day compared to Support Vector Machine (SVM) model.
| Item Type: | Monograph (Project Report) |
|---|---|
| Subjects: | T Technology T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraan Awam (School of Civil Engineering) > Monograph |
| Depositing User: | Mr Engku Shahidil Engku Ab Rahman |
| Date Deposited: | 09 Jun 2022 09:09 |
| Last Modified: | 09 Jun 2022 09:09 |
| URI: | http://eprints.usm.my/id/eprint/52829 |
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