The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2024.0151148
PDF

Feature Selection Methods Using RBFNN-Based to Enhance Air Quality Prediction: Insights from Shah Alam

Author 1: Siti Khadijah Arafin
Author 2: Ahmad Zia Ul-Saufie
Author 3: Nor Azura Md Ghani
Author 4: Nurain Ibrahim

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 11, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: This study examines the predictive efficiency of several feature selection approaches in air quality models aimed to predict next-day PM2.5 concentrations in Shah Alam, Malaysia. Air pollution in urban areas is a significant public health concern, and accurate prediction models are essential for timely interventions. However, determining the most important parameters to include in these models remains difficult, especially in complex urban areas with several pollution sources. To address this, we employed three different feature selection methods and applied them to a dataset comprising 43,824 air quality data points provided by the Department of Environmental Malaysia. The data set contained ten variables, such as gas pollutants and meteorological indicators. Each feature selection approach determined top eight variables to include in a Radial Basis Function Neural Network (RBFNN) model. The results showed that ReliefF outperformed Lasso and mRMR in terms of accuracy, specificity, precision, F1 Score, and AUROC, making it the most effective feature selection method for this study. This study contributes to the body of knowledge on air quality modelling by emphasising the relevance of using proper feature selection techniques that are suited to the specific characteristics of the dataset and urban area. Furthermore, it proposes that future study should look into the use of ReliefF-RBFNN in other settings, such as suburban and rural areas, as well as hybrid feature selection approaches to improve prediction performance across several context.

Keywords: Lasso; mRMR; PM2.5 concentration; RBFNN; ReliefF

Siti Khadijah Arafin, Ahmad Zia Ul-Saufie, Nor Azura Md Ghani and Nurain Ibrahim, “Feature Selection Methods Using RBFNN-Based to Enhance Air Quality Prediction: Insights from Shah Alam” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151148

@article{Arafin2024,
title = {Feature Selection Methods Using RBFNN-Based to Enhance Air Quality Prediction: Insights from Shah Alam},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151148},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151148},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Siti Khadijah Arafin and Ahmad Zia Ul-Saufie and Nor Azura Md Ghani and Nurain Ibrahim}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org