基于深度学习的空气质量预测模型的建立

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI:10.1109/ICCC51575.2020.9345081

R. Guo, Yuanjing Ma, Shuai Wang, Yiming Du, Shihai Wang

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引用次数: 2

摘要

在现有空气质量预测模型的基础上，提出了一种基于深度学习的空气质量预测方法。通过对预报资料、监测资料和气象资料的分析，建立了一种新的区域空气质量预报模型。该模型充分考虑了大气污染物浓度的时间变异性和空间分布特征，并引入气象数据作为协变量对研究区域内任意位置进行预测。

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Establishment of Air Quality Forecast Model Based on Deep Learning

According to the existing air quality forecasting model, this paper proposed an air quality forecasting method based on deep learning. By analyzing forecasting data, monitoring data and meteorological data, a new air quality forecasting model in the region is established. The model fully takes into account the time variability and spatial distribution characteristics of air pollutant concentration, and introduces meteorological data as covariates to predict any location in the study area.

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来源期刊

2020 IEEE 6th International Conference on Computer and Communications (ICCC)

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