Deep Learning Approach for Evaluating Air Pollution Using the RFM Model

Abstract

Air pollution is a required environmental and public health issue in India, with multiple municipalities repeatedly ranking among the most polluted in the world. This study leverages large datasets to construct a predictive model for forecasting air quality trends using a novel approach that integrates the Recency Frequency Monetary (RFM) model with deep learning. The research aims to efficiently quantify pollution events frequency and assess the impact of air quality variations on public health, offering a more flexible and adaptive system for air quality monitoring. 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.