Air Quality Prediction based on Neural Network

Abstract

Air pollution, driven by various human activities, has become a critical global issue with significant impacts on human health and the environment. Accurate forecasting and monitoring of air quality are essential to mitigate these effects. This paper introduces a novel deep learning model, AirPhyNet, which integrates atmospheric physics principles into its design, aiming to improve the precision of air quality predictions. The model is evaluated against several baseline models, demonstrating its robustness and effectiveness in forecasting air quality dynamics. The study emphasizes the importance of incorporating physical insights into deep learning models to enhance prediction accuracy and interpretability. The model's performance is compared systematically with existing baseline models, showcasing its superior accuracy and generalisation capabilities. The paper also discusses the use of machine learning algorithms for air quality prediction, highlighting the need for accurate results to inform public health measures and environmental strategies. The study utilizes real-world air quality datasets from Beijing and Shenzhen, China, and employs evaluation metrics to objectively compare the models. The results indicate that AirPhyNet outperforms state-of-the-art deep learning models and classical approaches, providing a promising tool for air quality forecasting in various environmental settings. The paper concludes by suggesting future research directions, including refining the model architecture and exploring its applications in real-world environmental management.