Random Forest Feature Selection for PM10 Pollution Concentration

Habeeb Balogun, Hafiz Alaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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There are already countless articles on strategies to limit human exposure to PM10 pollutants because of
their disastrous impact on the environment and people's well-being in the United Kingdom (UK) and
around the globe. Strategies such as imposing sanctions on places with higher levels of exposure,
dissuading non-environmentally friendly vehicles, motivating the use of bicycles for transportation, and
encouraging the use of eco-friendly fuels in industries. All these methods are viable options but will take
longer to implement. For this, efficient PM10 predictive machine learning is needed with the most
impactful features/data. The predictive model will offer more strategic avoidance techniques to this lethal
air pollutant, in addition to all other current efforts. However, the diversity of the existing data is a
challenge. This paper tries to solve this by bringing together traffic information, pollution concentration
information, geographical/built environment information, and meteorological information. Furthermore,
this paper applied random forest, which outperformed the decision tree and XGBoost in selecting the
most impactful features. As part of the discovery from this research work, it is now clearly discovered
that the height of buildings in a geographical area has a role to play in the dispersion of PM10.
Original languageEnglish
Subtitle of host publicationConfluence of Theory and Practice in the Built Environment: Beyond Theory into Practice
Place of PublicationFaculty of Environmental Design and Management Obafemi Awolowo University, Ile-Ife
PublisherObafemi Awolowo University, Ile-Ife
ISBN (Print)978-37119-9-7
Publication statusPublished - 6 Jul 2021


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