Harnessing Machine Learning for Accurate Smog Level Prediction: A Study of Air Quality in India

Accurate prediction of smog concentrations is needed to mitigate the harm of AP on public health and the environment. This research proposes a new method to combine machine learning (ML) models with live data from Central Pollution Control Board (CPCB) to fill in the smog prediction accuracy gaps. The data consist of hourly AQI readings from different towns in India which were preprocessed to adjust for missing values and normalize data before ML models. The algorithms were tested with 8 ML algorithms, and hyper-parameter settings were tuned using the GridSearchCV method. The results show that XG Boost Regressor (XGBR) and Extra Tree Regressor (ETR) models significantly surpass other ML algorithms and traditional techniques with better accuracy on predicting smog. These results are useful for policymakers and environmental agencies to implement sustainable air quality management.