计算机科学
领域(数学)
空气污染
污染物
理论(学习稳定性)
时间序列
特征(语言学)
预测建模
数据挖掘
空气污染物
集合(抽象数据类型)
频道(广播)
人工智能
系列(地层学)
数据集
卷积神经网络
机器学习
气象学
数学
地理
电信
古生物学
哲学
有机化学
化学
程序设计语言
纯数学
生物
语言学
作者
Fei Lei,Xuan Zhang,Yuning Yang
摘要
Air pollution is an environmental problem facing mankind today. Therefore, predicting the concentration of air pollutants in advance plays an important role in people's life and government decision-making. In this paper, a multi-channel asymmetric structure prediction model based on temporal convolutional network (TCN) is proposed. As TCN omits some feature information when learning time series features, increasing the number of channels will improve the receptive field of the model, cover longer historical information and extract more time series features. The influence of meteorological factors on the concentration of air pollutants is fully considered in the prediction model, which is used as an auxiliary factor to improve the prediction performance of the model. The concentration of air pollutants collected from the air monitoring station in Fushun City, Liaoning Province, is used as the data set to verify the effectiveness of the model, and the experimental comparison with other prediction models is conducted. The results show that the model proposed in this paper has more accurate prediction accuracy and stronger stability.
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