Hybrid RF-LSTM: An Advance Machine Learning Technique for Prediction of Air Pollution in Indian Cities

India is grappling with a serious air pollution crisis that threatens the health and well-being of its large population. Primary sources of air pollution include industrial emissions, vehicular exhaust, agricultural residue burning, and dust. Fine particulate matter (PM2.5), in particular, contributes to millions of deaths and substantial economic losses. Accurate prediction of the Air Quality Index (AQI) is vital for tackling air pollution, as it enables early warnings, supports data-driven decision-making, and facilitates the formulation of effective control measures. To improve AQI forecasting, this paper presents a novel hybrid approach combining Random Forest (RF) and Long Short-Term Memory (LSTM) techniques. The hybrid RF-LSTM model utilizes the RF algorithm to extract relevant features from the AQI dataset, which are then input into the LSTM network to capture temporal dependencies and long-term patterns in the sequential data. The AQI data, with extracted features for five Indian cities Delhi, Lucknow, Chandigarh, Jaipur and Gurugram, is input into the LSTM model, and the prediction results of the hybrid RF-LSTM model are then analysed and presented. The results indicate that the hybrid RF-LSTM approach outperforms both individual RF and LSTM models in predicting various AQI constituents, thereby offering improved support for addressing air pollution.