| 基于注意力机制的Transformer模型预测PM2.5浓度 |
| 摘要点击 1329 全文点击 194 投稿时间：2023-11-16 修订日期：2024-03-08 |
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| 中文关键词 PM2.5预测 多头注意力机制 Transformer 同源性 |
| 英文关键词 PM2.5 forecasting multi-head attention mechanism Transformer homology |
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| 中文摘要 |
| 采用北京市12个空气监测站点2013年3月至2016年12月期间的监测数据以及气象数据,利用皮尔逊系数方法探究影响PM2.5浓度的关键因素,构建一种基于多头注意力机制的改进Transformer模型对PM2.5浓度进行长期预测,并引入卷积神经网络模型（ResNet50）和长短期记忆网络模型（LSTM）进行比较,选用解释性方差（EVS）、决定系数（R2）、均方误差（MSE）和平均绝对误差（MAE）这4个指标对模型性能进行评价.结果表明,皮尔逊相关系数结果显示与PM2.5浓度极强相关的参数为PM10、SO2、NO2、CO和大气压力（PRES）,强相关的参数为露点温度（DEWP）,这与Transformer模型自动筛选的偏好设置一致.Transformer模型的MSE和R2分别为0.009 μg·m-3和0.925,与ResNet50和LSTM比较,MSE分别降低了91.09%和30.77%,R2分别提高了38.05%和4.65%.Transformer模型可以更好地捕捉因气象条件突变影响产生的短期污染变化和具有显著季节变化的长期趋势,拟合效果在几个模型中表现优异,为实现PM2.5浓度的长期预测提供了一种新的方法.此外,根据消融实验发现在数据输入或人为设置偏好后,Transformer的R2增幅较小,分别为2.31%和1.51%,说明Transformer模型对同源性PM10数据有较强的抗干扰能力. |
| 英文摘要 |
| An improved transformer model based on a multi-head attention mechanism was constructed for long-term prediction of PM2.5 concentration. The monitoring data and meteorological data from 12 air monitoring stations in Beijing from March 2013 to December 2016 were collected and used in the transformer model. The Pearson coefficient was used to explore the key factors affecting PM2.5 concentration. A convolutional neural network model （ResNet50） and long short-term memory network model （LSTM） were introduced for comparison and explanatory variance （EVS）, coefficient of determination （R2）, mean square error （MSE）, and mean absolute error （MAE） were selected to evaluate the performance of the model. The Pearson coefficient results showed that PM10, SO2, and NO2, CO, and atmospheric pressure （PRES） were highly correlated with PM2.5 concentration, and dew point temperature （DEWP） was strongly correlated with PM2.5 concentration, which was consistent with the preference setting of the model’s automatic screening. The MSE and R2 of the transformer model are 0.009 μg·m-3 and 0.925, respectively, which decreased by 91.09% and 30.77% of MSE and increased by 38.05% and 4.65% of R2, respectively, when compared with ResNet50 and LSTM. The transformer model could capture short-term pollution changes caused by sudden changes in meteorological conditions and long-term trends with significant seasonal changes. The fitting effect of the transformer was excellent among several models, providing a novel method for long-term prediction of PM2.5 concentration. In addition, ablation experiments revealed that the increase in R2 of the transformer was relatively small after data input or manually setting preferences, with only a 2.31% and 1.51% increase, respectively, indicating that the transformer model had strong anti-interference ability for PM10 homologous data. |
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