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计算机系统应用:2020,29(6):265-270
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Elman神经网络在优化空气预报模式结果中的应用
(1.中国科学院 沈阳计算技术研究所, 沈阳 110168;2.中国科学院大学, 北京 100049;3.辽宁省沈阳生态环境监测中心, 沈阳 110000;4.中国医科大学附属第四医院, 沈阳 110032;5.沈阳市第二十二中学, 沈阳 110000)
Application of Elman Neural Network in Optimizing Air Forecast Model Results
(1.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Shenyang Ecological Environment Monitoring Center of Liaoning Province, Shenyang 110000, China;4.The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, China;5.Shenyang Twenty-Second Middle School, Shenyang 110000, China)
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投稿时间:2019-10-22    修订日期:2019-11-20
中文摘要: 空气质量与人们的生活息息相关, 空气质量的预测结果是进行空气质量控制的依据. 因此, 提高空气质量的预测精度是本文研究的重点. CMAQ (Community Multiscale Air Quality modeling system)和CAMx (Comprehensive Air quality Model with extensions)是两种常用的空气质量数值模式, 其工作原理是通过大气物理化学方法模拟污染物传输转化过程, 进而预测空气质量. 空气质量数值模式的输入文件质量会影响到空气质量的预测精度, 为了提高空气质量预测的准确率, 本文提出了一种基于Elman神经网络的优化方法, 该方法在CMAQ和CAMx两种空气质量数值模式基础上利用Elman神经网络优化预测结果. 首先, 运行空气质量模式CMAQ和CAMx得到预测结果, 然后对预测结果进行预处理, 处理后的预测数据和实测数据一起作为Elman神经网络的输入, 进行模型的训练, 最后得到神经网络模型. 通过对测试数据集的验证和分析, 实验结果表明, 该方法表现出比单一空气质量数值模式更高的准确率.
Abstract:The air quality is closely related to people’s lives. The prediction results of air quality are the basis for air quality control. Therefore, how to improve the prediction accuracy of air quality is the focus of this study. The Community Multiscale Air Quality modeling system (CMAQ) and the Comprehensive Air quality Model with extensions (CAMx) are two commonly used numerical models of air quality. The prediction principles are based on atmospheric physical and chemical methods to simulate the process of pollutant transmission and conversion, and then air quality is predicted. The quality of the input files of the air quality numerical model affects the accuracy of the air quality prediction. In order to improve the accuracy of air quality prediction, this study proposes a method based on Elman neural network. This method uses Elman neural network to optimize the prediction results of two air quality numerical models of CMAQ and CAMx. First, this study runs the air quality mode CMAQ and CAMx to get the prediction results, and then pre-process the prediction results. The processed prediction data and the measured data are used as the input of the Elman neural network for model training and finally get the neural network model. Through the verification and analysis of the test data set, the experimental results show that the method shows higher accuracy than the single air quality numerical model.
文章编号:7441     中图分类号:    文献标志码:
基金项目:辽宁省“兴辽英才计划”(XLYC1808004)
引用文本:
张镝,于海飞,刘闽,杜毅明,金继鑫,曹吉龙,赵思彤.Elman神经网络在优化空气预报模式结果中的应用.计算机系统应用,2020,29(6):265-270
ZHANG Di,YU Hai-Fei,LIU Min,DU Yi-Ming,JIN Ji-Xin,CAO Ji-Long,ZHAO Si-Tong.Application of Elman Neural Network in Optimizing Air Forecast Model Results.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):265-270

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