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

Damla Mengus; Bihter Daş

doi:10.35377/saucis...1560377

EN

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

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

Supporting Institution

This study was not supported by any institution.

Ethical Statement

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

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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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Damla Mengus ^*
0000-0002-6706-0230
Türkiye

Bihter Daş
0000-0002-2498-3297
Türkiye

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 1970 Volume: 8 Number: 1

DOI

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

IZ

https://izlik.org/JA32PN96CK

Cite

RIS / Bibtex

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

1.Mengus D, Daş B. Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. SAUCIS. 2025;8(1):89-111. doi:10.35377/saucis.1560377

Chicago

Mengus, Damla, and Bihter Daş. 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.

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

[1]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, Mar. 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 1, 2025): 89-111. https://doi.org/10.35377/saucis. 1560377.

JAMA

1.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, Mar. 2025, pp. 89-111, doi:10.35377/saucis. 1560377.

Vancouver

1.Damla Mengus, Bihter Daş. Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting. SAUCIS. 2025 Mar. 1;8(1):89-111. doi:10.35377/saucis. 1560377

Öz

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

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite