电子学报 ›› 2022, Vol. 50 ›› Issue (6): 1410-1427.DOI: 10.12263/DZXB.20200258
所属专题: 长摘要论文
张波1,3, 陆云杰1, 秦东明2,4, 邹国建1
收稿日期:
2020-03-14
修回日期:
2021-07-25
出版日期:
2022-06-25
作者简介:
基金资助:
ZHANG Bo1,3, LU Yun-jie1, QIN Dong-ming2,4, ZOU Guo-jian1
Received:
2020-03-14
Revised:
2021-07-25
Online:
2022-06-25
Published:
2022-06-25
Supported by:
摘要:
城市空气污染因空间扩散特性呈现出区域内的浓度高关联性.因此如何通过多个空气污染监测站的时空数据预测特定目标地点的污染情况,以解决站点分布不匀的问题,是一个重要的研究工作.本文结合空气污染物因素特性和气象因素的多维度影响,提出了一个利用区域内多站点空间监测数据实现特定目标站点的空气污染物浓度预测模型.该模型通过多层卷积神经网络(Convolutional Neural Network,CNN)实现城市多站点污染物浓度与气象数据之间的维度关联特征及空间关联特征学习,进而利用基于多层长短期记忆网络(Long Short-Term Memory,LSTM)的自编码网络实现多站点浓度的时序关联特征分析.实验通过真实数据集验证,所提出的预测模型获得了高于传统机器学习污染物浓度预测模型的预测准确度,且在多个城市数据集上验证了模型的泛化能力.
中图分类号:
张波, 陆云杰, 秦东明, 等. 一种卷积自编码深度学习的空气污染多站点联合预测模型[J]. 电子学报, 2022, 50(6): 1410-1427.
Bo ZHANG, Yun-jie LU, Dong-ming QIN, et al. A Multi-Site Joint Air Pollution Prediction Model Based on Convolutional Auto-Encoder Deep Learning[J]. Acta Electronica Sinica, 2022, 50(6): 1410-1427.
参数名称 | 参数值 |
---|---|
数据集时间跨度 | 2014.5.13—2018.3.24 |
训练集 | 2014.5.13—2017.12.31 |
测试集 | 2018.1.1—2018.3.24 |
预测未来时长 | 24 h |
历史数据时长 | 72 h |
矩阵大小 (站点数*特征数) | [ |
最大迭代次数(Epoch) | 100 |
表1 数据集详细参数
参数名称 | 参数值 |
---|---|
数据集时间跨度 | 2014.5.13—2018.3.24 |
训练集 | 2014.5.13—2017.12.31 |
测试集 | 2018.1.1—2018.3.24 |
预测未来时长 | 24 h |
历史数据时长 | 72 h |
矩阵大小 (站点数*特征数) | [ |
最大迭代次数(Epoch) | 100 |
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
BP | 15.857 | 10.284 | 0.875 | 181 s |
RNN | 15.670 | 9.398 | 0.905 | 197 s |
LSTM | 9.688 | 7.139 | 0.958 | 304 s |
CNN | 27.068 | 21.048 | 0.980 | 871 s |
CNN+LSTM | 22.902 | 19.033 | 0.897 | 980 s |
CNN-RNN | 15.710 | 12.932 | 0.951 | 1219 s |
CNN-LSTM | 9.173 | 7.108 | 0.975 | 1342 s |
CAE-Learning | 8.880 | 7.001 | 0.980 | 2249 s |
表2 每个模型的RMSE、MAE、相关系数和训练时间
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
BP | 15.857 | 10.284 | 0.875 | 181 s |
RNN | 15.670 | 9.398 | 0.905 | 197 s |
LSTM | 9.688 | 7.139 | 0.958 | 304 s |
CNN | 27.068 | 21.048 | 0.980 | 871 s |
CNN+LSTM | 22.902 | 19.033 | 0.897 | 980 s |
CNN-RNN | 15.710 | 12.932 | 0.951 | 1219 s |
CNN-LSTM | 9.173 | 7.108 | 0.975 | 1342 s |
CAE-Learning | 8.880 | 7.001 | 0.980 | 2249 s |
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 23.153 | 21.909 | 0.899 | 1576 s |
CAE-Learning | 18.249 | 16.535 | 0.945 | 2785 s |
表3 模型在杭州市数据集下的RMSE、MAE、相关系数和训练时间
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 23.153 | 21.909 | 0.899 | 1576 s |
CAE-Learning | 18.249 | 16.535 | 0.945 | 2785 s |
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 24.529 | 20.693 | 0.893 | 1657 s |
CAE-Learning | 18.249 | 16.535 | 0.945 | 2269 s |
表4 模型在苏州市数据集下的RMSE、MAE、相关系数和训练时间
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 24.529 | 20.693 | 0.893 | 1657 s |
CAE-Learning | 18.249 | 16.535 | 0.945 | 2269 s |
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 37.881 | 28.744 | 0.407 | 1867 s |
CAE-Learning | 18.735 | 15.662 | 0.923 | 2487 s |
表5 模型在重庆市数据集下的RMSE、MAE、相关系数和训练时间
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 37.881 | 28.744 | 0.407 | 1867 s |
CAE-Learning | 18.735 | 15.662 | 0.923 | 2487 s |
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 36.158 | 24.504 | 0.601 | 1578 s |
CAE-Learning | 18.727 | 15.260 | 0.903 | 2278 s |
表6 模型在北京市数据集下的RMSE、MAE、相关系数和训练时间
模型 | RMSE | MAE | 相关系数 | 训练时间 |
---|---|---|---|---|
CNN-LSTM | 36.158 | 24.504 | 0.601 | 1578 s |
CAE-Learning | 18.727 | 15.260 | 0.903 | 2278 s |
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