Air Quality Index Prediction Using GCN + LSTM Model

Innovative solutions are needed to limit the pollution on ecosystems and the human health, since it continues to be a key global concern. The creation of a sophisticated predictive analytics model for predicting air quality in urban settings, especially in smart cities, is the main goal of this research. The intricate dynamics of urban air pollution are difficult for traditional forecasting techniques to fully capture, underscoring the need for more advanced techniques. This project addresses this challenge by proposing a system that uses Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. The GCN component captures spatial relationships between monitoring stations, leveraging the underlying graph structure of the urban environment. In parallel, the LSTM model captures temporal dynamics in air pollution data, enabling accurate forecasting based on historical observations. The proposed system architecture encompasses data collection, preprocessing, feature engineering, and model training stages. It constructs a graph representation of the urban environment, incorporates spatial and temporal dependencies, and optimizes model performance through hyperparameter tuning. Real-time prediction and monitoring capabilities empower stakeholders to make informed decisions and implement timely interventions to manage air pollution effectively. By combining cutting-edge techniques from data science, environmental science, and urban planning, this project offers a comprehensive solution to the challenges of urban air pollution. Successful implementation of this predictive analytics model has the potential to revolutionize environmental management practices, improve public health outcomes.