Air quality prediction model based on deep learning hybrid framework.

Area of Science:

• Environmental science and engineering
• Artificial intelligence in environmental monitoring
• Atmospheric science and air quality management

Background:

• Accelerating industrialization and modernization exacerbate air pollution issues globally.
• Accurate air quality prediction is crucial for effective pollution prevention and control strategies.
• Existing models may lack the precision needed for complex urban atmospheric dynamics.

Purpose of the Study:

• To develop a novel hybrid model for enhanced urban air quality prediction.
• To improve the accuracy and reliability of forecasting PM2.5 concentrations.
• To provide robust technical support for air pollution management.

Main Methods:

• A hybrid model (CBLA) integrating one-dimensional Convolutional Neural Networks (1D-CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an attention mechanism.
• 1D-CNNs for deep feature extraction from air quality data.
• BiLSTM for time-series analysis, attention mechanism for feature optimization, and XGBoosting for integrating predictions with meteorological data.

Main Results:

• The CBLA model demonstrated excellent performance in air quality prediction tasks.
• Experimental evaluations using Beijing's datasets confirmed the model's effectiveness.
• The hybrid approach successfully captured complex temporal dependencies and influential factors.

Conclusions:

• The proposed CBLA model offers a powerful tool for precise urban air quality forecasting.
• The integration of CNN, BiLSTM, attention, and XGBoosting significantly enhances prediction accuracy.
• This approach provides a valuable contribution to air pollution control and environmental protection efforts.