Authors : Homa Rizvi; Sunny Kumar; Dr. Yusuf Perwej; Farheen Siddiqui; Dr. Nikhat Akhtar

Volume/Issue : Volume 10 - 2025, Issue 12 - December

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DOI : https://doi.org/10.38124/ijisrt/25dec1067

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Abstract : Air pollution has been steadily increasing throughout a number of nations over the course of the last several decades as a direct result of human activity, urbanization, and industrialization. Deep Learning (DL) and Machine Learning (ML) approaches have made significant contributions to the development of methodologies in a variety of areas, including the prediction, planning, and uncertainty analysis of smart cities and urban progress in the present situation. As a result of the fast development in both population and industry, a significant number of major cities have experienced severe air quality (AQ) problems. This research offers a system that is based on ensemble deep learning and was built for the purpose of forecasting the levels of air pollution in smart environments. For the purpose of improving the accuracy and resilience of predictions, the system that has been presented incorporates a number of different deep learning models. These models include Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). The ensemble technique is an efficient method for addressing the inherent complexity that are present in air quality data. This is accomplished by using the strengths of many models. By allowing proactive control of air quality and informed policy-making, the installation of such a system has the potential to make a substantial contribution to the sustainability and liability of urban settings. The integration of other data sources, such as data on traffic and industrial activities, and the investigation of sophisticated ensemble methods are two potential future paths for study. Both of these approaches are intended to further enhance prediction performance.

Keywords : Air Pollution, Convolutional Neural Networks (CNN), Smart Environmental Factors, Ensemble Learning, Deep Learning, Pollution Prevention, Sustainability.

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