Predicting Air Quality Index with Recurrent Neural Networks and Meta-heuristic Algorithms

Abstract

Millions of people worldwide suffer from the impacts of air pollution, a significant health risk. The metric Air Quality Index (AQI) serves as a crucial tool, providing valuable insights into current air quality conditions and potential health risks. This study utilizes two datasets: one from Wuhan City and the other from Shanghai. The features utilized for forecasting the AQI include PM2.5, PM10, SO2, NO2, O3, CO, l-temp, h-temp, temp, wet, wind, Hecto-pascal Pressure Unit (hpa), visibility, precipitation, and cloud content. This work focuses on developing models to predict AQI for a given data by comparing Long Short Term Memory (LSTM) and its variants, including Bidirectional LSTM (BiLSTM), Stacked LSTM, and Gated Recurrent Unit (GRU) models. Additionally, Particle Swarm Optimization is utilized as an evolutionary feature selection method. © 2024 IEEE.