Application of Data Mining Techniques for Predicting Air Pollution for a Sunshine City (Harare Case Study)
- Author
- Mufandaedza, Edspartia. A
- Title
- Application of Data Mining Techniques for Predicting Air Pollution for a Sunshine City (Harare Case Study)
- Abstract
-
Air pollution is a significant environmental hazard that poses a danger to all living organisms,
as fresh and good quality air is vital for survival. Human activities such as automotive
transportation, agricultural practices, industrialization, mining, and fossil fuel combustion
contribute to air pollution by releasing harmful pollutants like sulfur dioxide, nitrogen dioxide,
carbon monoxide, and particulate matter into the air. The polluted air we breathe can cause
various health issues. Therefore, there is a need for an effective system to predict air pollution
and improve environmental conditions. To address this issue, advanced techniques, such as
data mining, can be used to predict air pollution in smart cities. In this study, a multivariate
multistep Time Series data mining technique using the random forest algorithm was employed
to predict air pollution levels. The model uses past data to make predictions, reducing
complexity and improving effectiveness and practicality. This approach can provide more
reliable and accurate decisions for environmental protection departments in smart cities. The
software was able to forecast whether the weather will be good or bad on a certain day - Date
- 2023
- Publisher
- BUSE
- Keywords
- Data Mining Techniques
- Air Pollution
- Supervisor
- Mr. Muzurura. O
- Item sets
- Department of Computer Science
