Comparative Study of Forecasting Models for Smart Campus Air

Abstract

Air quality monitoring has become increasingly critical in urban environments, especially in densely populated smart campuses situated in tropical regions. This study presents a comparative evaluation of three predictive models CNN-GRU, LSTM, and Random Forest, for forecasting air pollution levels, specifically particulate matter concentration (PM), using real-time sensor data. The data were collected from an IoT-based monitoring system built with NodeMCU ESP8266 devices deployed on campus. Each model was trained and evaluated using performance metrics including the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results indicate that the Random Forest model achieved the highest predictive accuracy with R² = 0.9073, MAE = 123.31, and RMSE = 274.45, outperforming both LSTM (R² = 0.8341) and CNN-GRU (R² = 0.8714). The hybrid CNN-GRU model, although capable of capturing both spatial and temporal dependencies, required larger data volumes and longer training times. The LSTM model, while effective in modeling time-series data, demonstrated a tendency to overfit when data was limited. This study highlights the practical advantages of Random Forest in modeling complex environmental data under limited resource constraints, while also demonstrating the potential of hybrid deep learning architectures. These findings contribute to the development of efficient air quality prediction systems that support health-conscious decision-making and environmental management strategies in tropical innovative campus environments.

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