时间精度与空间信息对神经网络模型预报PM2.5浓度的影响

以北京市为例, 利用2015—2018年空气质量监测站台资料, 通过BP神经网络、LSTM网络及CNNLSTM混合模型等多种模型, 分析时间精度和空间信息对PM_2.5浓度预报的影响。结果表明, 神经网络模型的效果普遍比多元线性回归模型好; 增加输入数据的时间精度能显著地提高 PM_2.5浓度日均值预报的准确率; 当输入数据的时间精度从一天提高到6小时后, LSTM模型的平均绝对误差从27.39 μg/m³降至20.59 μg/m³, 这种效果的提升在显著变好和显著变差的天气情况下更明显; 华北地区PM_2.5浓度分布有明显的时空特征, 第一空间模态为同增同减, 第二空间模态为南北反向; 北京市PM_2.5浓度与内蒙古、河北及天津等地区前一天的PM_2.5相关。利用CNN-LSTM混合模型学习华北地区PM_2.5的时空信息, 能进一步提高北京市PM_2.5浓度的预报水平, 使得误差降低至17.36 μg/m³。

Impacts of Temporal Resolution and Spatial Information on Neural-Network-Based PM2.5 Prediction Model

Taking Beijing as an example and using the data of air quality monitoring stations from 2015 to 2018, the impacts of temporal resolution and spatial information on the PM_2.5 concentration prediction were analyzed by a BP neural network, an LSTM network, and a CNN-LSTM hybrid model. The results show that neural network models are generally better than the multi-linear regression model. Increasing the temporal resolution of the input data can significantly improve the accuracy of the predicted daily average PM_2.5 concentration. When the temporal resolution of the input data increases from one day to 6 hours, the mean absolute error of the LSTM model reduces from 27.39 μg/m³ to 20.59 μg/m³. This improvement is more obvious when the weather is significantly getting better or getting worse. The distribution of PM_2.5 concentration in North China has distinct spatial and temporal characteristics. The first spatial mode is a uniformly increasing or decreasing mode, and the second one is a north/south dipole mode. The analysis shows that the concentration of PM_2.5 in Beijing is related to the PM_2.5 in Inner Mongolia, Hebei, and Tianjin of the previous day. The CNN-LSTM hybrid model, trained with the spatialtemporal information of PM_2.5 in North China, can further improve the predictability of PM_2.5 in Beijing. It further reduces the mean absolute error to 17.36 μg/m³.