AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction

• URL: http://arxiv.org/abs/2402.03784v2
• Date: Wed, 7 Feb 2024 02:10:11 GMT
• Title: AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
• Authors: Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long, Gao Cong, Jingyuan Wang
• Abstract summary: This paper presents a novel approach named Physics guided Neural Network for Air Quality Prediction (AirPhyNet) We leverage two well-established physics principles of air particle movement (diffusion and advection) by representing them as differential equation networks. Experiments on two real-world benchmark datasets demonstrate that AirPhyNet outperforms state-of-the-art models for different testing scenarios.
• Score: 40.58819011476455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions. Although traditional data-driven models have shown promise in this domain, their long-term prediction accuracy can be limited, especially in scenarios with sparse or incomplete data and they often rely on black-box deep learning structures that lack solid physical foundation leading to reduced transparency and interpretability in predictions. To address these limitations, this paper presents a novel approach named Physics guided Neural Network for Air Quality Prediction (AirPhyNet). Specifically, we leverage two well-established physics principles of air particle movement (diffusion and advection) by representing them as differential equation networks. Then, we utilize a graph structure to integrate physics knowledge into a neural network architecture and exploit latent representations to capture spatio-temporal relationships within the air quality data. Experiments on two real-world benchmark datasets demonstrate that AirPhyNet outperforms state-of-the-art models for different testing scenarios including different lead time (24h, 48h, 72h), sparse data and sudden change prediction, achieving reduction in prediction errors up to 10%. Moreover, a case study further validates that our model captures underlying physical processes of particle movement and generates accurate predictions with real physical meaning.

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