J. Shrivastava

Explainable Deep Learning for Air Quality Index Prediction: Integrating Meteorological and Traffic Data for Anomaly Identification

A explainable deep learning model taking into account meteorological parameters, traffic-flow factors and historical pollutant information to predict the AQI and detect abnormal pollution events is designed and shows that the prediction accuracy and interpretability can be achieved concurrently.