CFP last date
20 October 2025
Call for Paper
November Edition
IJCA solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 20 October 2025

Submit your paper
Know more
The week's pick
Responsive image
Zero Trust Architecture Implementation in Enterprise Networks: Evaluating Effectiveness Against Cyber Threats

Stephen Kofi Dotse Samuel Yao Sebuabe Augustus Obeng Silas Asani Abudu Edna Awisie Pappoe
Random Articles
Reseach Article

GAs-based Weight Optimization of Multilayer Perceptron Neural Networks for Air Quality Prediction

by Anthony Stone, Alaa Sheta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 38
Year of Publication: 2025
Authors: Anthony Stone, Alaa Sheta
10.5120/ijca2025925667

Anthony Stone, Alaa Sheta . GAs-based Weight Optimization of Multilayer Perceptron Neural Networks for Air Quality Prediction. International Journal of Computer Applications. 187, 38 ( Sep 2025), 1-9. DOI=10.5120/ijca2025925667

@article{ 10.5120/ijca2025925667,
author = { Anthony Stone, Alaa Sheta },
title = { GAs-based Weight Optimization of Multilayer Perceptron Neural Networks for Air Quality Prediction },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2025 },
volume = { 187 },
number = { 38 },
month = { Sep },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number38/gas-based-weight-optimization-of-multilayer-perceptron-neural-networks-for-air-quality-prediction/ },
doi = { 10.5120/ijca2025925667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-09-23T00:36:29+05:30
%A Anthony Stone
%A Alaa Sheta
%T GAs-based Weight Optimization of Multilayer Perceptron Neural Networks for Air Quality Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 38
%P 1-9
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Air pollution, particularly the concentration of particulate matter (PM10), poses significant risks to public health and environmental quality. Therefore, this study proposes a Multilayer Perceptron (MLP) optimized by a Genetic Algorithm (GA) to develop a predictive modeling approach for estimating PM10 levels, capturing the complex interactions between atmospheric conditions and pollutants. The model incorporates twelve key input variables, including meteorological conditions and pollutant indicators such as temperature, CO, NO, NO2, NOx, PM2.5, O3, RH, SO2, wind direction (WD), wind speed (WS), and lagged PM10 values. The dataset was divided into 70% for training and 30% for testing. The recommended model demonstrated a remarkable capacity to identify complex patterns in the PM10 data by generating remarkably accurate predictions and a strong correlation with actual results during training. These results demonstrate the effectiveness of evolutionary optimization in enhancing FNN-based models for predicting air quality.

References
  1. M. R. Miller and D. E. Newby, “Air pollution and cardiovascular disease: Car sick,” Cardiovascular Research, vol. 116, no. 2, pp. 279–294, Feb. 2020.
  2. P. Petropoulou, I. Artopoulou, I. Kalemikerakis, and O. Govina, “Environment and public health: Air pollution and chronic diseases,” Environmental Sciences Proceedings, vol. 26, no. 1, 2023.
  3. R. Roy and A. D’Angiulli, “Air pollution and neurological diseases: current state highlights,” Frontiers in Neuroscience, vol. 18, Mar. 2024.
  4. U. Sagheer, S. Al-Kindi, S. Abohashem et al., “Environmental pollution and cardiovascular disease: Part 1 of 2: Air pollution,” JACC: Advances, vol. 3, no. 2, Feb. 2024.
  5. Y.-J. Zhang, T.-T. Xu, Y.-L. Luan, H.-M. Shen, and Y. Guo, “Navigating the complexity of exposure to multiclass organic pollutants in respirable size-resolved particles and implications for oxidative potential,” Environment International, vol. 202, p. 109646, 2025.
  6. M. Krishan, S. Jha, J. Das, A. Singh, M. K. Goyal, and C. Sekar, “Air quality modelling using long short-term memory (lstm) over nct-delhi,” Air Quality, Atmosphere Health, vol. 12, no. 8, pp. 899–908, 2019.
  7. R. Rohde and R. Muller, “Air pollution in china: Mapping of concentrations and sources,” PLoS ONE, vol. 10, no. 8, pp. 1–14, 2015.
  8. G. Geng, Y. Liu, Y. Liu, S. Liu, J. Cheng, L. Yan, N. Wu, H. Hu, D. Tong, B. Zheng, Z. Yin, K. He, and Q. Zhang, “Efficacy of china’s clean air actions to tackle pm2.5 pollution between 2013 and 2020,” Nature Geoscience, vol. 17, pp. 987–994, 09 2024.
  9. World Health Organization, “Ambient (outdoor) air quality and health,” Fact sheet, World Health Organization, Oct. 2024, last updated 24 October 2024. [Online]. Available: https://www.who.int/news-room/fact-sheets/ detail/ambient-(outdoor)-air-quality-and-health
  10. A. Abdelfatah, S. A. Mokhtar, A. Sheta, and B. Solaiman, “Forecast global carbon dioxide emission using swarm intelligence,” International Journal of Computer Applications, vol. 77, no. 12, pp. 1–5, 2013.
  11. P. Orellano, J. Reynoso, N. Quaranta, A. Bardach, and A. Ciapponi, “Short-term exposure to particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and ozone (O3) and all-cause and cause-specific mortality: Systematic review and meta-analysis,” Environment International, vol. 142, p. 105876, 2020.
  12. M. Alkasassbeh, A. F. Sheta, H. Faris, and H. Turabieh, “Prediction of PM10 and TSP air pollution parameters using artificial neural network autoregressive, external input models: A case study in salt, jordan,” Middle East Journal of Scientific Research, vol. 14, no. 7, pp. 999–1009, 2013.
  13. H. Turabieh, A. F. Sheta, M. Braik, and E. Kovaˇc-Andri´c, “A layered recurrent neural network for imputing air pollutants missing data and prediction of NO2, O3, PM10, and PM2.5,” in Forecasting in Mathematics, A. A. Jaoude, Ed. Rijeka: IntechOpen, 2020, ch. 3.
  14. A. F. Sheta and H. Faris, “Influence of nitrogen-di-oxide, temperature and relative humidity on surface ozone modeling process using multigene symbolic regression genetic programming,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 6, 2015.
  15. World Health Organization, “Types of pollutants,” Web page, WHO– Air Quality and Health (Health Impacts section), 2025, accessed 25 July 2025. [Online]. Available: https://www. who.int/teams/environment-climate-change-and-health/ air-quality-and-health/health-impacts/types-of-pollutants
  16. A. Abdelfatah, S. Mokhtar, A. Sheta, and B. Solaiman, “Forecast global carbon dioxide emission using swarm intelligence,” International Journal of Computer Applications, vol. 77, pp. 1–5, 09 2013.
  17. R. Selvaraj, R. Samuel, G. R., E. Krishnamoorthy, and S. Jeyakumar, “A neural network model for short-term prediction of surface ozone at tropical city,” International Journal of Engineering Science and Technology, vol. 2, oct 2010.
  18. A. Afzali, M. Rashid, B. Sabariah, and M. Ramli, “Pm10 pollution: Its prediction and meteorological influence in Pasir- Gudang, johor,” IOP Conference Series: Earth and Environmental Science, vol. 18, no. 1, p. 012100, feb 2014.
  19. A. Azzini, “A new genetic approach for neural network design and optimization,” Ph.D. Thesis, Universit`a degli Studi di Milano, Milano, Italy, 2007, defended March 8, 2007; academic year 2005/06.
  20. H. Faris, I. Aljarah, and S. Mirjalili, “Training feedforward neural networks using multi-verse optimizer for binary classification problems,” Applied Intelligence, vol. 45, no. 2, pp. 322–332, 2016.
  21. M. Braik, A. Sheta, and H. Al-Hiary, “Hybrid neural network models for forecasting ozone and particulate matter concentrations in the republic of china,” Air Quality, Atmosphere & Health, vol. 13, pp. 839–851, 2020.
  22. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Let a biogeography-based optimizer train your multi-layer perceptron,” Information Sciences, vol. 269, pp. 188–209, 2014.
  23. T. Thaher, M. Awad, M. Aldasht, W. A. Deabes, A. F. Sheta, and H. K. Chantar, “An enhanced evolutionary-based feature selection approach using grey wolf optimizer for the classification of high-dimensional biological data,” Journal of Universal Computer Science, vol. 28, no. 5, pp. 499–539, 2022.
  24. M. Braik, A. F. Sheta, and H. Al-Hiary, “A novel metaheuristic search algorithm for solving optimization problems: Capuchin search algorithm,” Neural Computing and Applications, vol. 33, no. 7, p. 2515–2547, 2021.
  25. M. S. Braik, A. F. Sheta, H. Turabieh, and H. Al-Hiary, “A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm,” Soft Computing, vol. 25, no. 104, pp. 8459–8477, 2021.
  26. B. Al-Himyari, H. Al-khafaji, and N. Hussain, “Explorationexploitation tradeoffs in metaheuristics: A review,” Asian Journal of Applied Sciences, vol. 12, 01 2025.
  27. A. Sheta and A. Abdel-Raouf, “Stock market price forecasting using metaheuristic search algorithms: A comparative analysis,” International Journal of Computing, vol. 23, no. 4, p. 702–708, 2024.
  28. A. Sheta, H. Faris, M. Braik, and S. Mirjalili, “Natureinspired metaheuristics search algorithms for solving the economic load dispatch problem of power system: A comparison study,” in Applied Nature-Inspired Computing: Algorithms and Case Studies, ser. Springer Tracts in Nature-Inspired Computing, N. Dey, A. S. Ashour, and S. Bhattacharyya, Eds. Springer, Singapore, 2019.
  29. A. F. Sheta, H. Faris, and E. Oznergiz, “Improving production quality of a hot rolling industrial process via genetic programming model,” International Journal of Computer Applications in Technology, vol. 49, no. 3/4, p. 239–250, 2014.
  30. A. Sheta, A. Ali, A. Baareh, and S. Aljahdali, “Meta-heuristic search algorithms for solving the economic load dispatch problem,” in 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022, pp. 87–92.
  31. A. Sheta, M. S. Braik, and S. Aljahdali, “Genetic algorithms: A tool for image segmentation,” in 2012 International Conference on Multimedia Computing and Systems, 2012, pp. 84– 90.
  32. A. Sheta, “Restoration of medical images using genetic algorithms,” in 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2017, pp. 1–6.
  33. A. F. Sheta, “Analogue filter design using differential evolution,” International Journal of Bio-Inspired Computation, vol. 2, no. 3, pp. 233–241, 2010.
  34. A. F. Sheta and A. Al-Afeef, “A gp effort estimation model utilizing line of code and methodology for NASA software projects,” in Proceedings of the 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010). Cairo, Egypt: IEEE, Nov. 2010, pp. 1003–1008.
  35. “Software effort estimation for NASA projects using genetic programming,” Journal of Intelligent Computing, vol. 1, no. 3, p. 146–156, 2010.
  36. Z. Al-Rahamneh, M. Reyalat, A. F. Sheta, S. Bani-Ahmad, and S. Al-Oqeili, “A new software reliability growth model: Genetic-programming-based approach,” Journal of Software Engineering and Applications, vol. 4, no. 8, p. 476–481, 2011.
  37. A. F. Sheta and A. H. Abdel-Wahab, “Identification of nonlinear communication channel using evolutionary volterra timeseries,” in Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99), vol. 1. IEEE, 1999, pp. 425–431.
  38. D. Whitley, “Genetic algorithms and neural networks,” in Genetic Algorithms in Engineering and Computer Science, J. Periaux and G. Winter, Eds. John Wiley & Sons, Jul. 1998.
  39. M. Sazli, “A brief review of feed-forward neural networks,” Communications Faculty of Science, University of Ankara, vol. 50, pp. 11–17, 01 2006.
  40. A. Mert, N. Kjljc¸, E. Bilgili, and A. Akan, “Breast cancer detection with reduced feature set,” Computational and Mathematical Methods in Medicine, vol. Article ID 265138, p. 11 pages, 05 2015.
  41. I. Aljarah, H. Faris, and S. Mirjalili, “Evolving neural networks using bird swarm algorithm for data classification and regression applications,” Cluster Computing, vol. 22, no. 1, pp. 299–317, 2019.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Multilayer Perceptron Weight Optimization Metaheuristics Genetic Algorithm Optimization

Powered by PhDFocusTM