PM2.5 concentration forecast using Hybrid models over Urban cities in India

Air pollution poses a substantial risk to both public health and the environment. Accurate forecasting of air quality is crucial in mitigating its detrimental impacts. The existing forecast method of air quality in India is computationally intensive and is not economical; hence, we utilize Advanced Machine and Deep Learning Models to forecast air quality. The objective of this research is to develop a novel hybrid model integrating Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) models to forecast concentrations over Kanpur. The study involved comprehensive data collection (Secondary air quality and meteorological data from the Central Pollution Control Board), analysis, and experimentation with multiple models. Root mean square error (RMSE) and coefficient of determination (R² score) are used for model validation. MLP-XGBoost-LSTM hybrid model works well with a decreased RMSE (12.6 μg/m³ ) and increased R² score (0.96) compared to individual models (XGBoost- 37 μg/m³, MLP-39 μg/m³, and LSTM-41 μg/m³).  The significance of the research lies in its potential to provide highly accurate forecasts, even with limited computational resources. These findings have significant implications for environmental policy, public health in heavily polluted regions, and the broader utilization of machine learning in environmental science.