Abstract
In most cities, the monitoring stations are sparse, and air pollution is affected by various internal and external factors. To promote the performance of regional pollution prediction, a deep spatio-temporal dense network model is proposed in this paper. First, the inverse distance weighted (IDW) space interpolation algorithm is used to construct historical emission data with insufficient station records. Based on the properties of spatial-temporal data, a deep spatial-temporal dense network model is designed to predict air pollution in each region. Finally, several experiments are conducted on the real dataset of Hangzhou. The results show that combing IDW spatial interpolation and deep spatial-temporal dense network model can effectively predict the regional air pollution and achieve superior performance compared with ARIMA, CNN, ST-ResNet, CNN-LSTM.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grant 61871427, National Key R&D Program of China under Grant 2016YFC0201400, Provincial Key R&D Program of Zhejiang Province under Grant 2017C03019 and International Science and Technology Cooperation Program of Zhejiang Province for Joint Research in High-tech Industry under Grant 2016C54007.
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Wu, Q., She, Q., Jiang, P., Yang, X., Wu, X., Lin, G. (2021). Deep Spatio-Temporal Dense Network for Regional Pollution Prediction. In: Liu, Q., Liu, X., Shen, T., Qiu, X. (eds) The 10th International Conference on Computer Engineering and Networks. CENet 2020. Advances in Intelligent Systems and Computing, vol 1274. Springer, Singapore. https://doi.org/10.1007/978-981-15-8462-6_10
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DOI: https://doi.org/10.1007/978-981-15-8462-6_10
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