基于阶段式时序注意力网络的PM<sub>2.5</sub>鲁棒预测

陆瑶; 杨洁; 邵智娟; 朱聪聪

doi:10.13205/j.hjgc.202110013

基于阶段式时序注意力网络的PM_2.5鲁棒预测

doi: 10.13205/j.hjgc.202110013

陆瑶¹,
杨洁^1, ,,
邵智娟¹,
朱聪聪²

1. 苏州科技大学环境科学与工程学院, 江苏苏州 215009;
2. 上海大学计算机工程与科学学院, 上海 200444

基金项目:

国家自然科学基金面上项目（41571475）。

详细信息

作者简介:
陆瑶(1993-),女,硕士,主要研究方向为大气污染及其防治。Luyao0418@163.com

通讯作者:
杨洁(1972-),女,博士,教授,主要研究方向为环境管理与规划、大气污染及其防治。yjagnes@163.com

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出版历程
- 收稿日期: 2020-11-28
- 网络出版日期: 2022-01-26

PM_2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK

1. College of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China;
2. College of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

摘要

摘要: PM_2.5浓度的预测对于大气污染治理、改善环境质量等起到重要作用。受气象条件变化与大气污染物排放等多种因素的交叉影响，PM_2.5预测通常易受突变事件及噪声数据干扰。因此，基于对气象条件以及大气污染物与PM_2.5的相关性分析，提出阶段式时序注意力网络模型（staged temporal-attention network，STAN），该方法融合多段注意力学习模块与循环神经网络，建模气象因素与大气污染物对PM_2.5浓度的交叉影响。统计分析北京市、上海市、广州市预测结果的绝对误差值，可知：1）对比广泛使用的单一类模型支持向量机（support vector machine，SVM）、长短期时序记忆方法（long short-term memory，LSTM）和多层感知机（multilayer perceptron，MLP），STAN可达到10%以上的性能领先；对比最新的融合类模型U型网络（U-net），STAN领先了7%的优势。2）以北京市冬季预测结果为例进行统计分析，STAN的预测值与实测值之间的拟合系数可有95.2%的性能领先。此外，在鲁棒性分析中发现，STAN在含有10%噪声的数据上进行预测，误差上升幅度仅为9.3%。结果表明：注意力机制与时序学习模块相结合能够深度挖掘PM_2.5变化规律并抑制噪声数据，且STAN模型可以进行PM_2.5浓度的鲁棒预测。
- PM_2.5鲁棒预测 /
- 分段注意力学习 /
- 时序记忆网络 /
- 排放预警
Abstract: The forecast of PM_2.5 concentration plays an important role in air pollution control and improvement of environmental quality. Affected by multiple factors such as changes in meteorological conditions and air pollutants emissions, the PM_2.5 forecast was usually susceptible to sudden changes and noise data. Therefore, in-depth exploration of the PM_2.5 concentration change law and modeling robust prediction models have become key steps in this task. Based on the analysis of the correlation between meteorological conditions and atmospheric pollutants on PM_2.5, a staged temporal-attention network (STAN) was proposed. This method combined a multi-stage attention module and a recurrent neural network (RNN) to model the cross-influence of meteorological factors and atmospheric pollutants on PM_2.5 concentration. Statistical analysis of the absolute error values of the prediction results of Beijing, Shanghai, and Guangzhou showed the following results:1) compared with the widely used support vector machine (SVM), long short-term memory (LSTM), and multilayer perceptron (MLP), the performance of the proposed method increased more than 10%. 2) compared with the latest fusion model U-net, the proposed STAN still achieved a decrease in the error of 7%. Taking Beijing's winter forecast results as an example for statistical analysis, the fitting coefficient between the predicted value of STAN and the measured value could reach 95.2%. In the robustness analysis, it was found that the error increased by only 9.3% when the proposed method ran on data with 10% noise. The above results proved that the combination of the attention mechanism and the temporal learning module could deeply mine the change law of PM_2.5 and suppress noise data. It also showed that the STAN could achieve the robust prediction of PM_2.5 concentration.
- PM_2.5 robust prediction /
- staged attention learning /
- recurrent neural networks /
- emissions warning

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