| id |
my.uniten.dspace-20660 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-206602023-05-05T00:17:21Z An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia Ellysia Jumin Ozone FYP 2 Sem 1 2019/2020 Malaysia is a developing country especially regions in Kuala Lumpur and Selangor. Current generation are indulging the comfort of life from the urbanization. Although it has positive advantageous yet somehow the disadvantageous slowly worsen life on earth through either direct or indirect impact contributing to adverse air quality. High level of tropospheric ozone concentration exceeding prescribed level by the department of environmental is an unfavorable air quality [17]. While ozone formation is a complex chemical reaction affected by many precursors, however in this study few ozone precursors has been identified and supported by past researches. Correlation between ozone and the precursors is based on Pearson Correlation Coefficient. Meteorology data such as wind speed and humidity are highly correlated followed by air quality data like nitrogen oxide, carbon monoxide, nitrogen dioxide and the least correlated Sulphur dioxide with ozone formation. Daytime dataset from 6:00 a.m. – 6:00 p.m. provide better model compared to 24-hour dataset for the best model Boosted Decision Tree Regression. Neural Network Regression with Gaussian Normalizer, Linear Regression and Neural Network with Min-Max Normalizer show over fit model. Among all three station of studies, S2 gives best data for the model development. Best model is successfully identified thus beneficial for any early prevention pertaining community safety in the near future. Further study on technique or by introducing complex input parameters can be applied to improve the model selected. 2023-05-03T15:11:32Z 2023-05-03T15:11:32Z 2019-10 https://irepository.uniten.edu.my/handle/123456789/20660 application/pdf |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| topic |
Ozone |
| spellingShingle |
Ozone Ellysia Jumin An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia |
| description |
FYP 2 Sem 1 2019/2020 |
| format |
|
| author |
Ellysia Jumin |
| author_facet |
Ellysia Jumin |
| author_sort |
Ellysia Jumin |
| title |
An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia |
| title_short |
An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia |
| title_full |
An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia |
| title_fullStr |
An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia |
| title_full_unstemmed |
An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia |
| title_sort |
advance ozone concentration prediction by implementing machine learning algorithms in selangor and kuala lumpur malaysia |
| publishDate |
2023 |
| _version_ |
1806426586339082240 |
| score |
13.222552 |