Comparative Analysis of Deep Learning and Statistical Models for Air Pollutants Prediction in Urban Areas

DOI

10.2991/978-94-6463-858-5_219How to use a DOI?

Keywords

Air Pollution Prediction; Deep Learning; CNN; LSTM; GRU; Statistical Models; Ensemble Models

Abstract

Air pollution is a hidden but serious public health risk that has been made worse by urbanization and industrialization. This study compares deep learning and statistical models for predicting urban air quality in order to lessen its consequences. LSTM, GRU, CNN, and ensemble combinations were among the methods that were assessed. With an accuracy of over 90%, the results demonstrate that CNN and CNN+LSTM perform better than alternative models. For stakeholders to more accurately forecast and control air quality, the ensemble approaches offer a strong framework.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. S. S.  Lakshmi Lavanya
AU  - M. Siri
AU  - L. Soumya
AU  - P. Shivamani
AU  - B.  Shiva
PY  - 2025
DA  - 2025/11/04
TI  - Comparative Analysis of Deep Learning and Statistical Models for Air Pollutants Prediction in Urban Areas
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2647
EP  - 2651
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_219
DO  - 10.2991/978-94-6463-858-5_219
ID  - Lavanya2025
ER  -