Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants

The project explores the use of machine learning approach to develop a model to predict the next day air quality based on the concentration of sulfur dioxide(SO2) and particulate matter(PM10) in the previous few days. The data source is the Hong Kong government environmental protection department (E...

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Main Author:	Nisar Nur Nasreen
Other Authors:	Wong Kin Shun, Terence
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2021
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/154128
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1541282023-07-07T18:37:31Z Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants Nisar Nur Nasreen Wong Kin Shun, Terence School of Electrical and Electronic Engineering EKSWONG@ntu.edu.sg Engineering::Electrical and electronic engineering The project explores the use of machine learning approach to develop a model to predict the next day air quality based on the concentration of sulfur dioxide(SO2) and particulate matter(PM10) in the previous few days. The data source is the Hong Kong government environmental protection department (EPD) website. The data from the EPD is used to train a machine learning model to recognise the days with high pollutant levels. After training, the machine learning model will be tested by making forecasts using the new measured pollutant data. The sci-kit learn module was used. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-12-19T10:53:51Z 2021-12-19T10:53:51Z 2021 Final Year Project (FYP) Nisar Nur Nasreen (2021). Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154128 https://hdl.handle.net/10356/154128 en A2410-202 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 Nisar Nur Nasreen Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants
description	The project explores the use of machine learning approach to develop a model to predict the next day air quality based on the concentration of sulfur dioxide(SO2) and particulate matter(PM10) in the previous few days. The data source is the Hong Kong government environmental protection department (EPD) website. The data from the EPD is used to train a machine learning model to recognise the days with high pollutant levels. After training, the machine learning model will be tested by making forecasts using the new measured pollutant data. The sci-kit learn module was used.
author2	Wong Kin Shun, Terence
author_facet	Wong Kin Shun, Terence Nisar Nur Nasreen
format	Final Year Project
author	Nisar Nur Nasreen
author_sort	Nisar Nur Nasreen
title	Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants
title_short	Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants
title_full	Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants
title_fullStr	Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants
title_full_unstemmed	Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants
title_sort	data driven air pollution forecast for so2 and pm10 atmospheric pollutants
publisher	Nanyang Technological University
publishDate	2021
url	https://hdl.handle.net/10356/154128
_version_	1772827326251270144

Data driven air pollution forecast for SO2 and PM10 atmospheric pollutants

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