Air Quality Prediction Based on VMD-CNN-BiLSTM-Attention

With the advancement of industrialization, air pollution has emerged as a critical global health and environmental concern. This study presents an air quality prediction model based on variational mode decomposition, a convolutional neural network, bidirectional long short-term memory, and an attention mechanism. The variational mode decomposition method is employed to decompose the Air Quality Index sequence, capturing different local characteristics of the original data. A hybrid model is constructed by integrating the convolutional neural network for feature extraction, the bidirectional long short-term memory for temporal pattern recognition, and the attention mechanism for focusing on significant data features. The model is optimized using the Grey Wolf Optimizer for hyperparameter tuning, thereby enhancing prediction accuracy. The proposed model is evaluated using air quality data from Changsha, China, covering the years 2015 to 2023. The results demonstrate that our model outperforms several other models in terms of mean absolute error, mean squared error, root mean squared error, and R-squared. This study provides a robust approach to air quality prediction, offering valuable insights for residents and policymakers.