Hourly Urban Air Pollution Level Prediction Using Numerical Modelling and Deep Learning Techniques

Abstract

Rapidly growing and urbanizing countries like India need forecasting of pollutants in the air to manage air quality. Traditional Artificial Neural Networks (ANN) have limited representation capacity for prediction, especially for complex, non-linear interactions among parameters in environmental data. To forecast hourly concentrations of PM2.5, this study presents an advanced deep learning model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a numerical-based ANN.The study measures impact factors such as PM10, NO_2,SO_2, and O_3 in a few chosen cities of India, influencing air pollution and determines the transition from traditional ANN to deep learning models. The result shows that the hybrid CNN-LSTM models reduce Root Mean Squared Error by 22-35% across pollutants, giving better results than classical ANN models.