PM<sub>2.5</sub> ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK

LU Yao; YANG Jie; SHAO Zhi-juan; ZHU Cong-cong

doi:10.13205/j.hjgc.202110013

Volume 39 Issue 10

Jan. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2021 > 39(10): 93-100

LU Yao, YANG Jie, SHAO Zhi-juan, ZHU Cong-cong. PM2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(10): 93-100. doi: 10.13205/j.hjgc.202110013

Citation:

LU Yao, YANG Jie, SHAO Zhi-juan, ZHU Cong-cong. PM_2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(10): 93-100. doi: 10.13205/j.hjgc.202110013

LU Yao, YANG Jie, SHAO Zhi-juan, ZHU Cong-cong. PM2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(10): 93-100. doi: 10.13205/j.hjgc.202110013

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PM_2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK

doi: 10.13205/j.hjgc.202110013

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

Received Date: 2020-11-28
Available Online: 2022-01-26

Abstract

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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References(30)

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