Advancements in Air Quality Forecasting using Artificial Intelligence: A Comprehensive Analysis

Ensuring good air quality is imperative for sustaining life on Earth. The escalating concerns of air quality in densely populated urban landscapes like Delhi, underscore the need for effective interventions. As urbanization and population growth exacerbate environmental issues, the deterioration of air quality becomes a paramount threat. This study explores how artificial intelligence can play a pivotal role in proactively managing air quality. The research evaluates the performance of three forecasting models for predicting PM2.5 and Air Quality Index (AQI) in Alipur, Delhi, India: Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and Seasonal Autoregressive Integrated Moving Average with exogenous variables (SARIMAX). Our analysis indicates that the Bi-LSTM model outperformed the others, demonstrating a Mean Absolute Error (MAE) of 0.0411 for PM2.5 and 0.0754 for AQI. This research not only highlights the effectiveness of AI models in capturing intricate temporal patterns but also emphasises their significance in formulating preemptive measures during episodes of poor air quality.