Abstract
Causal relationship mining of multi-dimensional meteorological time series data can reveal the potential connection between visibility and other influencing factors, and it is meaningful in terms of environmental management, air pollution chasing, and haze control. However, because causality analysis based on statistical methods or traditional machine learning techniques cannot represent the complex relationship between visibility and its influencing factors, as an extension of traditional Granger-causality analysis, we propose a causality mining method based on the seq2seq-LSTM deep learning model. The method can profoundly reveal the hidden relationship between different features and visibility and the regular pattern of bad weather, which can provide theoretical support for air pollution control.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (61702021), Beijing Natural Science Foundation (4174082), and General Program of Science and Technology Plans of Beijing Education Committee (SQKM201710005021).
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Liu, B., He, X., Li, J., Qu, G., Lang, J., Gu, R. (2020). Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_31
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DOI: https://doi.org/10.1007/978-981-15-3250-4_31
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