Research Article
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Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting

Year 2025, Volume: 8 Issue: 1, 89 - 111, 28.03.2025

Damla Mengus , Bihter Daş

https://doi.org/10.35377/saucis...1560377

Abstract

This study utilizes air pollution data from the Continuous Monitoring Center of the Ministry of Environment, Urbanization, and Climate Change in Turkey to predict various pollutants using three advanced deep learning approaches: LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and RNN (Recurrent Neural Network). Missing data in the dataset were imputed using the K-Nearest Neighbor (K-NN) algorithm to ensure data completeness. Furthermore, a data fusion technique was applied to integrate multiple pollutant enhancing the richness and reliability of the input features for modeling. The increasing air pollution issue, driven by factors such as population growth, urbanization, and industrial development, is a major environmental concern. The study evaluates these models to estimate pollutant concentrations and selects the most accurate, RNN, for forecasting air pollution over the next three years. Each prediction was assessed using performance metrics such as MAE, RMSE, and R² to ensure robust model evaluation. Visualization of the data and forecast results was achieved through methods like Box Plots, Violin Plots, and Point Scatter Graphs, making air quality information more accessible to general audiences. In terms of model performance, CNN achieved an R² of 0.88 for PM10 and 0.93 for SO2, while LSTM demonstrated an R² of 0.94 for PM10 and 0.95 for SO2. However, RNN emerged as the most accurate model, achieving an R² of 0.97 for both PM10 and SO2 forecasts. This model allows for forecasts of pollutant levels over a three-year period. The findings indicate that predictive modeling, combined with data fusion and visualization techniques, could significantly contribute to mitigating future uncertainties and enhance the comprehension of air quality patterns for non-expert audiences.

Keywords

Data prediction CNN RNN LSTM Data visualization

Ethical Statement

During this study, scientific research and publication ethics were followed.

Supporting Institution

This study was not supported by any institution.

Thanks

I would like to express my sincere gratitude to Assoc. Prof. Dr. Bihter Daş for her valuable contribution and guidance in the conduct of this study. The academic support and guidance she provided played a major role in the completion of this study.

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Year 2025, Volume: 8 Issue: 1, 89 - 111, 28.03.2025

Damla Mengus , Bihter Daş

https://doi.org/10.35377/saucis...1560377

Abstract

References

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  • P. A. Rani and Dr. V. Sampathkumar, “A novel artificial intelligence algorithm for predicting air quality by analysing the pollutant levels in air quality data in tamilnadu,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 5, p. 100234, Sep. 2023, doi: 10.1016/j.prime.2023.100234.
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  • B. Zhang et al., “Air pollutant diffusion trend prediction based on deep learning for targeted season—North China as an example,” Expert Systems with Applications, vol. 232, p. 120718, Dec. 2023, doi: 10.1016/j.eswa.2023.120718.
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  • T. D. Akinosho, M. Bilal, E. T. Hayes, A. Ajayi, A. Ahmed, and Z. Khan, “Deep learning-based multi-target regression for traffic-related air pollution forecasting,” Machine Learning with Applications, vol. 12, p. 100474, Jun. 2023, doi: 10.1016/j.mlwa.2023.100474.
  • M. Yılmaz, Y. Kara, H. C. Çulpan, G. Can, and H. Toros, “Detection and regional analysis of heatwave characteristics in İstanbul,” Sustainable Cities and Society, vol. 97, p. 104789, Oct. 2023, doi: 10.1016/j.scs.2023.104789.
  • J. González-Pardo, S. Ceballos-Santos, R. Manzanas, M. Santibáñez, and I. Fernández-Olmo, “Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain,” Science of The Total Environment, vol. 823, p. 153786, Jun. 2022, doi: 10.1016/j.scitotenv.2022.153786.
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There are 59 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Damla Mengus 0000-0002-6706-0230

Bihter Daş 0000-0002-2498-3297

Early Pub Date March 27, 2025
Publication Date March 28, 2025
Submission Date October 3, 2024
Acceptance Date January 9, 2025
Published in Issue Year 2025Volume: 8 Issue: 1

Cite

APA Mengus, D., & Daş, B. (2025). Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. Sakarya University Journal of Computer and Information Sciences, 8(1), 89-111. https://doi.org/10.35377/saucis...1560377
AMA Mengus D, Daş B. Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. SAUCIS. March 2025;8(1):89-111. doi:10.35377/saucis.1560377
Chicago Mengus, Damla, and Bihter Daş. “Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting”. Sakarya University Journal of Computer and Information Sciences 8, no. 1 (March 2025): 89-111. https://doi.org/10.35377/saucis. 1560377.
EndNote Mengus D, Daş B (March 1, 2025) Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. Sakarya University Journal of Computer and Information Sciences 8 1 89–111.
IEEE D. Mengus and B. Daş, “Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting”, SAUCIS, vol. 8, no. 1, pp. 89–111, 2025, doi: 10.35377/saucis...1560377.
ISNAD Mengus, Damla - Daş, Bihter. “Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 2025), 89-111. https://doi.org/10.35377/saucis. 1560377.
JAMA Mengus D, Daş B. Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. SAUCIS. 2025;8:89–111.
MLA Mengus, Damla and Bihter Daş. “Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, 2025, pp. 89-111, doi:10.35377/saucis. 1560377.
Vancouver Mengus D, Daş B. Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. SAUCIS. 2025;8(1):89-111.
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