Abstract
Air pollution has a negative impact on people’s health, and accurate prediction of future air pollutant concentrations is crucial for cities and individuals to take early warning and preventive measures against potential air pollution. In this paper, we propose an air pollutant prediction model, named CMLSTM, that well combines Mogrifier LSTM and CNN to predict a single pollutant for the next six hours using multi-site air pollutant data, meteorological data, and holiday information. Mogrifier LSTM can capture long-term air pollutant time-series features with richer contextual interactions, while CNN uses one-dimensional convolution to effectively model the spatial transport of air pollutants. We conduct experiments with four years of data from one city, and the results demonstrate CMLSTM has higher prediction accuracy than the baseline methods.
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Acknowledgement
This work was supported in part by the Inner Mongolia Science and Technology Plan Project (No. 2020GG0187), and Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software, Inner Mongolia Key Laboratory of Social Computing and Data Processing.
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Lian, M., Liu, J. (2022). Single Pollutant Prediction Approach by Fusing MLSTM and CNN. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_11
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DOI: https://doi.org/10.1007/978-3-031-10989-8_11
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