Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
Forecasting the air pollution index has become a popular topic in recent years due to the impact of air pollution on environmental and human health. The Support Vector Machine (SVM) is not only appropriate for object classification, regression analysis and pattern recognition, it can also be used in...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English |
| 出版: |
2021
|
| 主题: | |
| 在线阅读: | https://ir.uitm.edu.my/id/eprint/44440/1/44440.pdf |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| id |
my-uitm-ir.44440 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-uitm-ir.444402021-04-22T07:22:39Z Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani 2021-03-30 Zamani, Nur Fithrinnisaa Multivariate analysis. Cluster analysis. Longitudinal method Time-series analysis Air pollution and its control Forecasting the air pollution index has become a popular topic in recent years due to the impact of air pollution on environmental and human health. The Support Vector Machine (SVM) is not only appropriate for object classification, regression analysis and pattern recognition, it can also be used in time series forecasting. The work presented here aims to compare the accuracy of different types of the kernel function in Support Vector Machine (SVM) and to build a forecasting system for Kelantan’s air pollution index for PM2.5 using Support Vector Machine (SVM). The data used was provided by the Department of Environment (DOE) and was recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results of the model were analyzed by using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) kernel function with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index based on the model testing with 0.03868583 (MAE) and 0.06251793 (RMSE) for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah. 2021-03 Thesis https://ir.uitm.edu.my/id/eprint/44440/ https://ir.uitm.edu.my/id/eprint/44440/1/44440.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Computer & Mathematical Sciences |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| topic |
Multivariate analysis Cluster analysis Longitudinal method Time-series analysis Air pollution and its control |
| spellingShingle |
Multivariate analysis Cluster analysis Longitudinal method Time-series analysis Air pollution and its control Zamani, Nur Fithrinnisaa Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani |
| description |
Forecasting the air pollution index has become a popular topic in recent years due to the impact of air pollution on environmental and human health. The Support Vector Machine (SVM) is not only appropriate for object classification, regression analysis and pattern recognition, it can also be used in time series forecasting. The work presented here aims to compare the accuracy of different types of the kernel function in Support Vector Machine (SVM) and to build a forecasting system for Kelantan’s air pollution index for PM2.5 using Support Vector Machine (SVM). The data used was provided by the Department of Environment (DOE) and was recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results of the model were analyzed by using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) kernel function with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index based on the model testing with 0.03868583 (MAE) and 0.06251793 (RMSE) for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Zamani, Nur Fithrinnisaa |
| author_facet |
Zamani, Nur Fithrinnisaa |
| author_sort |
Zamani, Nur Fithrinnisaa |
| title |
Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani |
| title_short |
Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani |
| title_full |
Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani |
| title_fullStr |
Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani |
| title_full_unstemmed |
Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani |
| title_sort |
forecasting of kelantan’s air pollution index [ap] pm2.5 using support vector machine [svm] / nur fithrinnisaa zamani |
| granting_institution |
Universiti Teknologi Mara Perlis |
| granting_department |
Faculty of Computer & Mathematical Sciences |
| publishDate |
2021 |
| url |
https://ir.uitm.edu.my/id/eprint/44440/1/44440.pdf |
| _version_ |
1783734718631510016 |