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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >PM2.5 Prediction with a Novel Multi-Step-Ahead Forecasting Model Based on Dynamic Wind Field Distance
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PM2.5 Prediction with a Novel Multi-Step-Ahead Forecasting Model Based on Dynamic Wind Field Distance

机译：基于动态风场距离的新型多步超前预报模型的PM2.5预报

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摘要

Particulate matter with a diameter of less than 2.5 (PM ) has damaged public health globally for a decade. Accurate forecasts of PM concentration can provide early warnings to prevent the public from hazard exposure. However, existing methods have not considered the available spatiotemporal data sufficiently due to their architecture or inadequate input, and most neglected wind impact on spatiotemporal correlation when selecting related sites. To fill this gap, we proposed a long short-term memory-convolutional neural network based on dynamic wind field distance (LSTM-CNN-DWFD) to predict PM concentration of a specific site for the next 24 h. A KNN method based on dynamic wind field distance was developed and applied to select highly related sites considering wind impact. A local stateful LSTM model was employed to capture temporal correlations in historical air quality and meteorological data for each related site. Then, these temporal features were integrated as a spatiotemporal matrix, and input into CNN for extracting spatiotemporal correlation features. Weather forecasts were also integrated into the model to promote accuracy. Hourly PM data from 36 monitoring sites in Beijing, China collected from 1 May 2014 to 30 April 2015 were used as experimental dataset. Six-fold rolling origin method was employed to conduct experiments on each site, and the results of 216 experiments validated the performance of the proposed LSTM-CNN-DWFD model. The mean values of the next 1–6 h prediction were 0.85, 0.81, 0.76, 0.70, 0.64, and 0.59, respectively, showing a decrease trend, indicating that the prediction accuracy decreases as the prediction time increases. Comparisons of LSTM-CNN-DWFD results to results from six other methods show that it delivered higher accuracy PM predictions, with the mean RMSE (MAE) of 1–6, 7–12, and 13–24 h prediction were 43.90 (29.17), 57.89 (42.16), and 63.14 (47.64), respectively. The results also demonstrate that the sites selected based on dynamic wind field distance are more related to the central site than that based on geographical distance, also contributing to prediction accuracy.
机译：直径小于2.5（PM）的颗粒物已经在全球范围内损害了公共健康长达十年。准确的PM浓度预测可以提供预警，以防止公众暴露于危险中。然而，由于现有方法的架构或输入不足，以及在选择相关站点时风对时空相关性的影响最被忽略，因此现有方法尚未充分考虑可用的时空数据。为了填补这一空白，我们提出了一个基于动态风场距离（LSTM-CNN-DWFD）的长期短期记忆-卷积神经网络，以预测未来24小时特定位置的PM浓度。开发了一种基于动态风场距离的KNN方法，并将其应用于考虑风影响的高度相关站点。使用本地状态LSTM模型来捕获每个相关站点的历史空气质量和气象数据的时间相关性。然后，将这些时间特征整合为时空矩阵，并输入到CNN中以提取时空相关特征。天气预报也被集成到模型中以提高准确性。将2014年5月1日至2015年4月30日在中国北京36个监测点的每小时PM数据用作实验数据集。采用六倍滚动原点法在每个站点上进行实验，并且216个实验的结果验证了所提出的LSTM-CNN-DWFD模型的性能。下一个1-6小时预测的平均值分别为0.85、0.81、0.76、0.70、0.64和0.59，显示出下降的趋势，表明预测精度随着预测时间的增加而降低。 LSTM-CNN-DWFD结果与其他六种方法的结果比较表明，它提供了更高的精度PM预测，平均RMSE（MAE）的1–6、7–12和13–24 h预测为43.90（29.17） ，57.89（42.16）和63.14（47.64）。结果还表明，基于动态风场距离选择的站点比基于地理距离的站点与中心站点的相关性更高，这也有助于预测准确性。

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