A Multi-Input Bi-LSTM Autoencoder Model with Wavelet Transform for Air Quality Prediction

Air pollution is a serious global issue that affects the health of millions of people worldwide. Machine learning models have shown promise in accurate prediction of the air quality index (AQI), which plays a crucial role in controlling and mitigating their impact. However, existing approaches have limitations in capturing temporal dependencies and analyzing frequency domain relationships among pollutants. In this study, we propose a novel multi-input model based on Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, incorporating wavelet transformation for enhanced air quality prediction. The model first decomposes air quality data from neighboring regions into frequency components using wavelet transform, then extract valuable characteristic information and relationships using the Bi-LSTM module. This make our model effectively captures features across both temporal and frequency domains, leading to improved prediction accuracy. The evaluation results on the public air quality dataset show that our model achieves lowest mean absolute error (MAE) of 6.72 compared to existing methods, highlighting its potential for real-world applications in public health protection.