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基于深度神经网络与联邦学习的污染物浓度预测二次建模 被引量:3

Secondary Modeling of Pollutant Concentration Prediction Based on Deep Neural Networks with Federal Learning
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摘要 进入新世纪,伴随着我国经济的高速发展,我国很多地区空气污染情况相对严重,同时政府对于空气污染情况的关注度与治理力度也越来越高。当前对于我国空气质量影响最大的是O3,SO2,NO2,CO,PM10,PM2.5这6种污染物,因而对这6种污染物浓度进行预测预报,及时作出相应管控调整就成为了保障居民健康、建设美丽中国的迫切需求。目前污染物预测的主流方案是WRF-CMAQ预测系统,该系统基于污染物物化反应与气象模拟两部分构成。但因为当前对于如臭氧在内的污染物的生成机理等研究还有待深入,WRF-CMAQ模型的预测存在较大误差。因此采用了深度神经网络对污染物浓度进行二次建模的方式,来减少预测误差。同时,采用联邦学习方法,对于多个监测站使用联邦学习进行数据训练,提升模型泛化能力。实验结果表明,相比于一次WRF-CMAQ的一次预测结果,深度神经网络的方案在均方误差值上最多缩小到了3.93%。同时,采用联邦学习的方案相比于单个监测站点在广泛测试中最多提升了68.89%的性能。 In the new century,along with the rapid development of Chinese economy,air pollution in many areas of China is relatively serious,while the government is paying more and more attention to air pollution,and its efforts to control air pollution are increasing.Currently,six pollutants that have the greatest impact on China’s air quality are O3,SO2,NO2,CO,PM10,PM2.5.Therefore,predicting and forecasting the concentrations of the six pollutants and making corresponding control adjustments in time have become the urgent needs to protect the health of residents and build a beautiful China.At present,the mainstream solution for pollutant prediction is WRF-CMAQ prediction system,which is based on two parts,physical and chemical reaction of pollutants and meteorological simulation.However,due to the current research on the generation mechanism of pollutants such as ozone is still on the way,the prediction of WRF-CMAQ model has large errors.Therefore,this paper adopts a deep neural network for secondary modeling of pollutant concentrations to reduce the prediction error.At the same time,this paper adopts the federal learning method,and uses federal learning for data training for multiple monitoring stations to improve the model generalization ability.Experiment results show that the deep neural network scheme reduces the mean square error value to at most3.93%compared to the primary prediction results of one WRF-CMAQ.Moreover,the scheme with federal learning improves the perfor-mance by up to 68.89%compared to a single monitoring site in extensive tests.
作者 钱栋炜 崔阳光 魏同权 QIAN Dong-wei;CUI Yang-guang;WEI Tong-quan(College of Computer Science and Technology,East China Normal University,Shanghai 200062,China)
出处 《计算机科学》 CSCD 北大核心 2022年第S02期932-936,共5页 Computer Science
基金 国家自然科学基金(62272169)
关键词 联邦学习 深度神经网络 污染物浓度预测 WRF-CMAQ Federated learning Deep neural networks Pollutant concentration prediction WRF-CMAQ
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