Mohammed Nasser, Al-Subaie (2021) Air Pollution Index Estimation Model Based On Artificial Neural Network. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Kimia. (Submitted)
|
PDF
Download (1MB) | Preview |
Abstract
Environmental conservation efforts are always dealing with a complex problem because it involves a large number of variables. However, choosing a correct model structure, and optimum training algorithm with minimum complexity is crucial. Therefore, a dimensional reduction method was implemented based on the multiway principal component analysis (MPCA) method. Three models were built in first part; ozone estimation model, particulate matter 10 (PM10) estimation model, and air pollution index (API) estimation model. Six inputs were used in ozone and PM10 models, which are nitrogen oxides( NOx), carbon monoxide (CO), sulphur dioxides (SO2), wind speed, air temperature, and relative humidity. After that, ozone and PM 10 were used as input to the API estimation model. The result shows that the implementation of the MPCA has insignificant improvement on the overall correlation factor due to the high nonlinearity of data.
| Item Type: | Monograph (Project Report) |
|---|---|
| Subjects: | T Technology T Technology > TP Chemical Technology |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraan Kimia (School of Chemical Engineering) > Monograph |
| Depositing User: | Mr Engku Shahidil Engku Ab Rahman |
| Date Deposited: | 30 Aug 2022 03:48 |
| Last Modified: | 30 Aug 2022 03:48 |
| URI: | http://eprints.usm.my/id/eprint/54403 |
Actions (login required)
 |
View Item |
