A Deep Learning Approach For Predicting Air Quality Index Using Meteorological Data And Mathematical Forecasting Models

Abstract

Air pollution has become an extreme environmental ecological and societal concern, and this is shown to be more so in the urban areas where the increase in industrialization and vehicles pollute air. Air Quality Index (2017) indicates that the Air Quality Index (AQI) is a means of measurement allowing evaluation of the degree of pollution, yet the stratification of its dependence on the values of meteorological variables in relation to pollutants remains multifaceted. Based on the data and findings presented, the paper suggests a forecasting model on the application of deep learning Combinations of meteorological and mathematical forecasting models to predict AQI using temperature of air, humidity, wind speed, and pressure as independent variables. Long Short-Term Memory (LSTM) networks are used based on their ability to build upon temporal features whereas hybrid forecasting mechanics are used to build tackling of prediction accuracy. It is proved by experimental findings that the presented model performs better than the traditional statistical procedures providing more accurate short-term foresight. The paper has established the potential of deep learning in environmental monitoring and identified practical challenges such as it can only work with high-quality continuous data, has a high memory computation cost, and has limited the ability to widely generalize across geographical regions. Research into enhancing robustness, scalability, and transparency of AQI forecasting systems by including satellite imagery, real-time IoT sensor networks and explainable AI techniques should be carried out in the future.