Air Quality Index (AQI) Prediction Using Machine Learning and Deep Learning Approaches

Precise prediction of the Air Quality Index (AQI) is vital for the prevention of public health hazards and policymaking. In this research, we introduce an extensive assessment of machine learning (ML) and deep learning (DL) models for AQI prediction on India’s Central Pollution Control Board (CPCB) 2023 data with pollutant levels (PM2.5, PM10, NO2, SO2, CO, O3) and meteorological features. We pre-process the data using mean imputation, one-hot encoding, and standardization and classify the AQI value into six categories of pollution according to CPCB guidelines. Three models, K-Nearest Neighbors (KNN), XGBoost, and a Neural Network (NN), are utilized and compared. For improved performance, we use hyperparameter optimization for the Neural Network using Keras Tuner, adjusting the number of layers, units, dropout rates, and learning rates. The hyperparameter-optimized Neural Network attains 98.17% accuracy, outperforming conventional models (KNN: 85.39%, XGBoost: 72.91%) and attaining improved precision (98.32%), recall (98.17%), and F1-score (98.18%). Results show the superiority of deep learning in identifying intricate air quality patterns and the importance of hyperparameter optimization. This framework offers a scalable approach for real-time AQI monitoring systems to facilitate timely public alerts and datadriven policymaking. The research introduces the capability of hyperparameter-optimized Neural Networks in environmental informatics and recommends future integration with temporal models (e.g., LSTM) for dynamic forecasting.