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| 基于RNN-CNN集成深度学习模型的PM2.5小时浓度预测 | |
| 引用本文： | 黄婕,张丰,杜震洪,刘仁义,曹晓裴.基于RNN-CNN集成深度学习模型的PM2.5小时浓度预测[J].浙江大学学报(理学版),2019,46(3):370-379. |
| 作者姓名： | 黄婕 张丰 杜震洪 刘仁义 曹晓裴 |
| 作者单位： | 1.浙江大学 浙江省资源与环境信息系统重点实验室，浙江 杭州 310028 2.浙江大学 地理信息科学研究所，浙江 杭州 310027 |
| 基金项目： | 国家自然科学基金资助项目(41671391，41471313)；国家海洋公益性行业科研专项经费资助项目(201505003). |
| 摘 要： | 针对目前大部分PM2.5预测模型预测效果不稳定、泛化能力不强的现状，以记忆能力较强的循环神经网络（RNN）和特征表达能力较强的卷积神经网络（CNN）为基础，采取Stacking集成策略对两者进行融合，提出了RNN-CNN集成深度学习预测模型。该模型不仅充分利用时间轴上的前后关联信息去预测未来的浓度，而且在不同层次上将自动提取的高维时序数据通用特征用于预测，以保证预测结果的稳定性。最后，对集成之前的RNN、CNN和集成之后的RNN-CNN模型，以2016年中国大陆地区1 466个监测站点的空气质量数据为样本进行实例验证，结果表明，RNN-CNN在PM2.5时间序列预测上的表现明显优于集成之前的RNN和CNN，而且泛化误差更低，在34%站点上的拟合度超过0.97，该模型可用于大范围区域的PM2.5小时浓度预测。 |
| 关 键 词： | PM2.5小时浓度预测 RNN CNN 深度学习 集成学习 |
| 收稿时间： | 2018-01-23 |
Hourly concentration prediction of PM2.5 based on RNN-CNN ensemble deep learning model |
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| Jie HUANG,Feng ZHANG,Zhenhong DU,Renyi LIU,Xiaopei CAO.Hourly concentration prediction of PM2.5 based on RNN-CNN ensemble deep learning model[J].Journal of Zhejiang University(Sciences Edition),2019,46(3):370-379. | |
| Authors: | Jie HUANG Feng ZHANG Zhenhong DU Renyi LIU Xiaopei CAO |
| Affiliation: | 1.Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China 2.Department of Geographic Information Science, Zhejiang University, Hangzhou 310027, China |
| Abstract: | Most of the current PM2.5 prediction models show unstable prediction effect and weak generalization ability. This research aims to design a prediction model called RNN-CNN for PM2.5 hourly concentration prediction based on ensemble deep learning. We choose Recurrent Neural Network (RNN) with strong memory and Convolutional Neural Network (CNN) with strong feature expression ability as individual learners and choose Stacking, an ensemble learning technique, to combine RNN and CNN so that we can take full advantages of both in the forecast. RNN-CNN can not only use the contextual information on the timeline to predict the future concentration ,but also extract different levels of essential features from the high dimensional features for prediction, ensuring the stability of the forecast. We take the air quality data of 1 466 monitoring stations in mainland China in 2016 as samples to compare the performance of RNN-CNN with the individual learners RNN and CNN. Experiment results show that RNN-CNN performs better and achieves higher prediction accuracy and stronger generalization ability than the individual learners RNN and CNN, What’s more, its index of agreement on the 34% of test stations is higher than 0.97, which indicates that the ensemble deep learning model RNN-CNN can be effectively used for prediction of PM2.5 hourly concentration at large scales. |
| Keywords: | PM2 5 hourly concentration prediction RNN CNN deep learning ensemble learning |
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