Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants

Air pollution poses a major threat to our health and safety. Common air pollutants include nitrogen dioxide (NO2) and particulate matter 2.5 (PM2.5), also known as fine suspended particulates (FSP), which are emitted by motor vehicles and power plants. The concentration levels of these pollutants...

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Main Author: Yeo, Huang Ling
Other Authors: Wong Kin Shun, Terence
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/150125
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1501252023-07-07T18:12:47Z Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants Yeo, Huang Ling Wong Kin Shun, Terence School of Electrical and Electronic Engineering EKSWONG@ntu.edu.sg Engineering::Electrical and electronic engineering Air pollution poses a major threat to our health and safety. Common air pollutants include nitrogen dioxide (NO2) and particulate matter 2.5 (PM2.5), also known as fine suspended particulates (FSP), which are emitted by motor vehicles and power plants. The concentration levels of these pollutants vary from day to day and can affect our daily activities and our health. Due to the detrimental effects of air pollution, it is becoming increasingly important to be able to predict air quality. This project focused on air pollution caused by NO2 and PM2.5 pollutants in Hong Kong and how machine learning techniques can be utilised to predict the concentration levels of these pollutants in the following day. Specifically, tree-based regression techniques, including Gradient Boosted Regression Trees, were explored and their performances were then evaluated using metrics such as Mean Absolute Error and Root Mean Square Error. Bachelor of Engineering (Information Engineering and Media) 2021-06-12T08:23:30Z 2021-06-12T08:23:30Z 2021 Final Year Project (FYP) Yeo, H. L. (2021). Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150125 https://hdl.handle.net/10356/150125 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Yeo, Huang Ling
Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants
description Air pollution poses a major threat to our health and safety. Common air pollutants include nitrogen dioxide (NO2) and particulate matter 2.5 (PM2.5), also known as fine suspended particulates (FSP), which are emitted by motor vehicles and power plants. The concentration levels of these pollutants vary from day to day and can affect our daily activities and our health. Due to the detrimental effects of air pollution, it is becoming increasingly important to be able to predict air quality. This project focused on air pollution caused by NO2 and PM2.5 pollutants in Hong Kong and how machine learning techniques can be utilised to predict the concentration levels of these pollutants in the following day. Specifically, tree-based regression techniques, including Gradient Boosted Regression Trees, were explored and their performances were then evaluated using metrics such as Mean Absolute Error and Root Mean Square Error.
author2 Wong Kin Shun, Terence
author_facet Wong Kin Shun, Terence
Yeo, Huang Ling
format Final Year Project
author Yeo, Huang Ling
author_sort Yeo, Huang Ling
title Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants
title_short Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants
title_full Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants
title_fullStr Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants
title_full_unstemmed Data-driven one day ahead air Quality forecast for NO2 and PM2.5 pollutants
title_sort data-driven one day ahead air quality forecast for no2 and pm2.5 pollutants
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150125
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