Ensemble Transfer Learning for Air Quality Classification: A Robust Model for Environmental Monitoring

Venu K, Krishnakumar B, Sasipriyaa N, Deepak Raajan N, Dharaneesh S, Hari R P

2025

Abstract

One of the most urgent environmental issues facing the world today is air pollution, which has an immediate impact on ecosystems and human health. Predicting air quality accurately is crucial for mitigation plans and early warning systems. The classification of air quality into six predetermined categories-Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, and Hazardous/Severe-is examined in this paper using transfer learning techniques. A dataset of images representing air quality levels was used, with pre-trained models such as VGG16, ResNet, and InceptionV3 fine-tuned for this classification task. Transfer learning models, known for their efficiency in image-based tasks, were individually tested and compared based on classification accuracy and performance metrics. To further improve prediction accuracy, a novel ensemble approach was implemented, combining VGG16, ResNet, and EfficientNet into a unified model. The ensemble model achieved significantly higher accuracy compared to individual models, particularly in predicting complex air quality scenarios such as the Hazardous/Severe category. This improvement in accuracy underscores the potential of combining multiple pre-trained models in air quality prediction tasks, addressing the challenge of differentiating between closely related pollution levels. The results suggest that this ensemble approach not only enhances classification accuracy but also provides a more robust prediction framework for real-world applications. The proposed method shows promise for integration into real-time air quality monitoring systems, offering an effective tool for public health agencies to predict and respond to deteriorating air quality conditions. This study adds to the expanding corpus of research on transfer learning and how it's used in environmental monitoring.

Paper Citation

in Harvard Style

K V., B K., N S., N D., S D. and R P H. (2025). Ensemble Transfer Learning for Air Quality Classification: A Robust Model for Environmental Monitoring. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 91-98. DOI: 10.5220/0013609300004664

in Bibtex Style

@conference{incoft25,
author={Venu K and Krishnakumar B and Sasipriyaa N and Deepak Raajan N and Dharaneesh S and Hari R P},
title={Ensemble Transfer Learning for Air Quality Classification: A Robust Model for Environmental Monitoring},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013609300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Ensemble Transfer Learning for Air Quality Classification: A Robust Model for Environmental Monitoring
SN - 978-989-758-763-4
AU - K V.
AU - B K.
AU - N S.
AU - N D.
AU - S D.
AU - R P H.
PY - 2025
SP - 91
EP - 98
DO - 10.5220/0013609300004664
PB - SciTePress