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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| Andre forfattere: | |
| Format: | Final Year Project (FYP) |
| Sprog: | English |
| Udgivet: |
Nanyang Technological University
2021
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| Online adgang: | https://hdl.handle.net/10356/154128 |
| Summary: | 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. |
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