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- W3025092043 abstract "Fine-grained air pollution monitoring has attracted increasing attention worldwide. Even with an increasing amount of both static and mobile sensing systems, an inference algorithm is still essential to achieve a comprehensive understanding of the urban atmospheric environment. Conventional physical model-based methods are unable to involve all the influencing factors with limited prior knowledge, and data-driven methods lacking physical interpretation may result in bad generalization ability. This paper presents a multi-task learning scheme, which combines the physical model and the data-driven model with both merits. It enhances the data learning of a neural network with the aid of prior knowledge on atmospheric dispersion, and also controls the impact of the knowledge with a tunable weighting coefficient. Evaluations over a real-world deployment in Foshan, China show that, with the resolution of 500m $times 500text{m}times 15$ min, the proposed method outperforms the state-of-the-art ones with 7.9% error reduction and 6.2% correlation increase. Benefited from the physical knowledge, the neural network obtains stable performance with lower variance, as well as higher robustness against negative background conditions." @default.
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- W3025092043 date "2020-01-01" @default.
- W3025092043 modified "2023-09-28" @default.
- W3025092043 title "Enhancing the Data Learning With Physical Knowledge in Fine-Grained Air Pollution Inference" @default.
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- W3025092043 doi "https://doi.org/10.1109/access.2020.2993610" @default.
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