Abstract
The inference and prediction of fine-grained air quality are two important directions in urban air computing. Solving these two problems can provide useful information for urban environmental governance and residents’ health improvement. In this paper, we propose a general approach to solve these two problems with one model, while most other existing works use different models to solve them. Our model is based on deep bidirectional and unidirectional long short-term memory (DBU-LSTM) neural network, which can capture bidirectional temporal dependencies and spatial correlation from time series data containing spatial information. To infer and predict the air quality of the target region, we use the historical meteorological data of the target region and the historical air quality data of regions which are similar to the target. Urban heterogeneous data such as point of interest (POI) and road network are used to evaluate the similarities between urban regions. We also use a tensor decomposition method to complete the missing historical air quality data onto monitoring stations, which reduces the error of our model. We evaluated our approach on real data sources obtained in Beijing, and the results show its advantages over recent literature.
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Ge, L., Zhou, A., Liu, J., Li, H. (2019). A Novel Approach for Air Quality Inference and Prediction Based on DBU-LSTM. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_12
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DOI: https://doi.org/10.1007/978-3-030-26072-9_12
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