Predicting Air Quality with Deep Learning: CNN vs LSTM vs GRU

A comparative study of CNN, LSTM, and GRU architectures for time-series AQI forecasting, with insights on model selection and performance trade-offs.

Overview

Air quality prediction is a critical public health challenge. Accurate forecasting of PM2.5 and AQI values enables early warnings. This post compares three deep learning architectures evaluated on sensor data.

Models Compared

CNN: Captures local temporal patterns within fixed windows. Fast to train. Best for short-term forecasting.

LSTM: Handles long-range dependencies in time-series data. Uses gating mechanisms. Best when historical context is important.

GRU: Simplifies LSTM gating mechanisms. Achieves similar performance. Requires fewer parameters and trains faster.

Results Summary

CNN12.48.9FastLSTM10.17.2ModerateGRU10.37.4Moderate

Conclusion

CNN offers the best speed-accuracy trade-off for short-term prediction (1–6 hours). LSTM slightly outperforms GRU for 24-hour forecasting. GRU trains faster, making it ideal for real-time systems.