Random Forest Feature Selection for Particulate Matter (PM10) Pollution Concentration

Habeeb Balogun, Hafiz Alaka, Christian Nnaemeka Egwim, Saheed O. Ajayi

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

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Abstract

There are already countless articles on strategies to limit human exposure to particulate matter10 (PM10) pollution 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 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 identified. 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 solves this by (1) Bringing together numerous data sources into an Amazon web service big data platform and (2) Investigating which exact feature contributes best to building a high-performance PM10 machine learning predictive model. Examples of such data sources in this research include traffic information, pollution concentration information, geographical/built environment information, and meteorological information. Furthermore, this paper applied random forest in selecting the most impactful features due to its better performance over the decision tree Feature selection and XGBoost feature selection method. 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 in the dispersion of PM10.
Original languageEnglish
Title of host publicationEDMIC 2021 CONFERENCE PROCEEDINGS ENVIRONMENTAL DESIGN & MANAGEMENT INTERNATIONAL CONFERENCE
Subtitle of host publicationConfluence of Theory and Practice in the Built Environment: Beyond Theory into Practice
Place of PublicationIle-Ife, Nigeria
PublisherObafemi Awolowo University, Ile-Ife
Pages576-587
Number of pages12
ISBN (Print)978-37119-9-7
Publication statusPublished - 8 Jul 2021
EventEDMIC 2021: ENVIRONMENTAL DESIGN AND MANAGEMENT INTERNATIONAL CONFERENCE: CONFLUENCE OF THEORY AND PRACTICE IN THE BUILT ENVIRONMENT: BEYOND THEORY INTO PRACTICE - Obafemi Awolowo University, Ile-Ife, Nigeria
Duration: 6 Jul 20218 Jul 2021

Conference

ConferenceEDMIC 2021: ENVIRONMENTAL DESIGN AND MANAGEMENT INTERNATIONAL CONFERENCE
Abbreviated titleEDMIC 2021
Country/TerritoryNigeria
CityIle-Ife
Period6/07/218/07/21

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