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

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

LOGI – Scientific Journal on Transport and Logistics
Volume 15 (2024): Issue 1 (January 2024)
By: Caroline Mongina Matara,  Simpson Osano Nyambane,  Amir Okeyo Yusuf,  Elisha Akech Ochungo and  Afaq Khattak  
Open Access
|Jul 2024

Authors

Caroline Mongina Matara

carolinematara@students.uonbi.ac.ke

University of Nairobi, Department of Civil & Construction Engineering, Nairobi, Kenya

Simpson Osano Nyambane

sosano@uonbi.ac.ke

University of Nairobi, Department of Civil & Construction Engineering, Nairobi, Kenya

Amir Okeyo Yusuf

ayusuf@uonbi.ac.ke

University of Nairobi, Department of Chemistry, Nairobi, Kenya

Elisha Akech Ochungo

elishakech1@gmail.com

Multimedia University, Department of Civil, Faculty of Engineering and Technology (FoET), Nairobi, Kenya

Afaq Khattak

khattak@tongji.edu.cn

Tongji University, College of Transportation Engineering, Shanghai, China
DOI: https://doi.org/10.2478/logi-2024-0008 | Journal eISSN: 2336-3037
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Language: English
Page range: 85 - 96
Submitted on: Nov 14, 2023
Accepted on: Jun 3, 2024
Published on: Jul 17, 2024
Published by: Institute of Technology and Business in České Budějovice
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Air quality,
traffic pollution,
particulate matter,
Boruta algorithm,
machine learning
Related subjects:
Business and economics,
Business management,
Industries,
Transportation, logistics, air traffic, shipping

© 2024 Caroline Mongina Matara, Simpson Osano Nyambane, Amir Okeyo Yusuf, Elisha Akech Ochungo, Afaq Khattak, published by Institute of Technology and Business in České Budějovice
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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