Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli
The air pollution problems has received more attention during the last decades whereby there has been a significant increase in public awareness of the potential dangers caused by chemical pollutants and their effects both human beings and the environment. To overcome these problems, the need for ac...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2009
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://ir.uitm.edu.my/id/eprint/69109/1/69109.pdf |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| id |
my-uitm-ir.69109 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-uitm-ir.691092024-11-26T02:04:27Z Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli 2009 Amdam @ Ramli, Roshaslinie Neural Networks (Computer). Artificial intelligence Air pollution and its control The air pollution problems has received more attention during the last decades whereby there has been a significant increase in public awareness of the potential dangers caused by chemical pollutants and their effects both human beings and the environment. To overcome these problems, the need for accurate estimates of air pollutant index (API) becomes important. To achieve such estimation tasks, the use of artificial neural network (ANN) is regarded as an effective technique. The purpose of this paper, ANN trained with feed-forward back-propagation algorithm is used to estimate the air pollutant index (API). The API system normally includes the major air pollutants which are ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and suspended particulate matter of less than 10 microns in size (PM10). This method uses the past raw data values to estimate the API. The data collected comprises of data for the previous three month, beginning from October 2006 for Klang Valley areas which are Shah Alam, Klang, Petaling Jaya and Kuala Lumpur. The results indicate that the ANN model estimated API with good accuracy to more than 90%. 2009 Thesis https://ir.uitm.edu.my/id/eprint/69109/ https://ir.uitm.edu.my/id/eprint/69109/1/69109.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Hamzah, Norhayati |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Hamzah, Norhayati |
| topic |
Neural Networks (Computer) Artificial intelligence Air pollution and its control |
| spellingShingle |
Neural Networks (Computer) Artificial intelligence Air pollution and its control Amdam @ Ramli, Roshaslinie Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli |
| description |
The air pollution problems has received more attention during the last decades whereby there has been a significant increase in public awareness of the potential dangers caused by chemical pollutants and their effects both human beings and the environment. To overcome these problems, the need for accurate estimates of air pollutant index (API) becomes important. To achieve such estimation tasks, the use of artificial neural network (ANN) is regarded as an effective technique. The purpose of this paper, ANN trained with feed-forward back-propagation algorithm is used to estimate the air pollutant index (API). The API system normally includes the major air pollutants which are ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and suspended particulate matter of less than 10 microns in size (PM10). This method uses the past raw data values to estimate the API. The data collected comprises of data for the previous three month, beginning from October 2006 for Klang Valley areas which are Shah Alam, Klang, Petaling Jaya and Kuala Lumpur. The results indicate that the ANN model estimated API with good accuracy to more than 90%. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Amdam @ Ramli, Roshaslinie |
| author_facet |
Amdam @ Ramli, Roshaslinie |
| author_sort |
Amdam @ Ramli, Roshaslinie |
| title |
Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli |
| title_short |
Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli |
| title_full |
Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli |
| title_fullStr |
Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli |
| title_full_unstemmed |
Estimation of air pollutant index (API) in Klang valley using artificial neural network (ANN) / Roshaslinie Amdam @ Ramli |
| title_sort |
estimation of air pollutant index (api) in klang valley using artificial neural network (ann) / roshaslinie amdam @ ramli |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2009 |
| url |
https://ir.uitm.edu.my/id/eprint/69109/1/69109.pdf |
| _version_ |
1818587923723845632 |