Classification of Particulate Matter (PM2.5) Concentrations Using Feature Selection and Machine Learning Strategies

Classification of Particulate Matter (PM2.5) Concentrations Using Feature Selection and Machine Learning Strategies

This research employed machine learning approaches to classify acceptable or non-acceptable particulate matter (PM2.5) concentrations using a dataset that was obtained from the Nairobi expressway road corridor. The dataset contained air quality data, traffic volume, and meteorological data. The Boru...

Full description

Saved in:
Bibliographic Details
Main Authors: Matara Caroline Mongina, Nyambane Simpson Osano, Yusuf Amir Okeyo, Ochungo Elisha Akech, Khattak Afaq
Format: Article
Language:English
Published: Sciendo 2024-01-01
Series:Logi
Subjects:
air quality
traffic pollution
particulate matter
boruta algorithm
machine learning
Online Access:https://doi.org/10.2478/logi-2024-0008
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items