Citation :

Vetri Selvi R, Sathish Babu R, "Environmental Pollution Management through Air Quality Prediction- A Deep Learning Based Approach," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 9, pp. 285-303, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I9P121

Abstract

‘Good health and well-being’ is the third Sustainable Development Goal formulated by the United Nations. Air pollution is one of the major issues that affects the good health of people across the globe. The quality of air we as ‘humans’ breathe is vital for leading a quality and healthy life. This research attempts to demonstrate the application of deep learning models in the prediction of the air quality index of select Indian cities. This research makes use of the techniques Random Forest, XGBoost, and LightGBM. It has also developed an advanced Deep Learning model for the prediction of the Air Quality Index. The developed models have been tested against a dataset on the air quality of multiple Indian cities. The performance evaluation revealed that the advanced deep learning model with additional layers has rendered better performance than the traditional model Random Forest, XGBoost and LightGBM. The research also gives insights into how deep learning models could be implemented on a real-time basis across all cities in India in order to prevent environmental pollution and ensure sustainable living of human beings on earth.

Keywords

Air Quality Index, Deep learning, Environmental Pollution, Indian Cities, SDG.

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