Multi-Sensor Data Fusion for PM2.5 Concentration Prediction Based on Machine Learning

Accurate PM2.5 prediction is the foundation of an effective air quality monitoring system, which can timely formulate pollution mitigation strategies. This article proposes an advanced machine learning based framework that integrates multi-source environmental sensor data, including SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, CO, PM10 and NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> measurement values, to improve prediction accuracy and real-time processing capabilities. Enhance data generation and model interpretability through meteorological constraints. Combining spatiotemporal features with time series analysis to dynamically model and capture the diffusion and accumulation effects of pollution. By using random forest optimization and integrating Conditional Generative Adversarial Networks(CGAN) to generate samples, model training and validation can be achieved. This model combines spatial interdependence and temporal dynamics between monitoring stations to capture complex pollution patterns and achieve closed-loop control in intelligent environmental systems. The experimental results show that compared with similar methods, the model proposed in this paper effectively improves the prediction accuracy, and the real-time processing capability of the system is helpful for real-time policy adjustments in emission management.