Deep Learning Approach for Evaluating Air Pollution Using the RFM Model

Abstract

Air pollution in India has become a serious environmental and public health issue, with numerous cities continuously ranking among the most polluted in the world. Large datasets are required for constructing prediction models that foresee air quality trends and levels of pollution. The research effort attempts to identify promptly with RFM in Deep Learning, quantify the frequency of pollution events, and assess the monetary impact of air quality variations on public health. As a result, a large volume of air quality data provided by RFM ( Recency, Frequency, and Monetary )will be flexible and frequently handled and analyzed. In this research, The performance of the integrated RFM technology is examined using Python and Google Colab, and the simulation results are compared to air pollution information from neural networks for structures in additional data using existing air quality monitoring systems in India. Performance examination of both regression and classification techniques in RFM. The execution of RFM can be one of the models and its potential to enhance air quality monitoring and urban sustainability.