Air Pollutant Concentration Prediction Based on GRU Method
Air Pollutant Concentration Prediction Based on GRU Method
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DOI：
10.1088/1742-6596/1168/3/032058
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
X. Zhou;Jianjun Xu;P. Zeng;Xiankai Meng
中科院分区：
文献类型：
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作者：
X. Zhou;Jianjun Xu;P. Zeng;Xiankai Meng
近年来，中国政府越来越重视生态文明建设，治理大气污染是其中一个必要环节。针对大气污染物的时间序列预测问题，提出了一种基于深度学习方法的时间序列预测模型。本文以北京市PM2.5小时浓度信息和气象信息为输入。通过GRU模型，按春、夏、秋、冬四季训练4个模型，并利用相应的测试集评价4个模型对相应季节PM2. 5的预测效果。经过反复实验和不断调整模型参数，对模型的预测误差和预测精度进行了分析比较，验证了该方法的可行性和优越性。结果表明，基于GRU模型的模型预测精度较高，该方法对大气污染物的时间序列预测是有效的。
In recent years, the Chinese government has paid more and more attention to the construction of ecological civilization, and the governance of air pollution is one of the necessary links. Aiming at the problem of the time series prediction of air pollutants, this paper develops a time series prediction model based on deep learning method. In this paper, Beijing’s hourly PM2.5 concentration information and weather information are used as input. Through GRU model, four models are trained according to the four seasons of spring, summer, autumn, and winter, and the effects of the four models on predicting the corresponding seasonal PM2.5 are evaluated by using corresponding test sets. After repeated experiments and constant adjustment of model parameters, the prediction error and prediction accuracy of the model are analyzed and compared, then the feasibility and advantages of this method are verified. The results delineate that the prediction accuracy of the model based on the GRU model is high, and the method is valid for the time series prediction of air pollutants.