引用本文:许治国.利用Keras构建神经网络在空气质量预测中的应用[J].环境监控与预警,2018,10(5):18-21
XU Zhi guo.Application of Neural Network in Air Quality Forecast by Keras[J].Environmental Monitoring and Forewarning,2018,10(5):18-21
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利用Keras构建神经网络在空气质量预测中的应用
许治国
广东省环境信息中心,广东 广州 510308
摘要:
利用python 语言搭建了一整套空气质量神经网络预测系统,底层利用Keras设计并建立了基于tensorflow的神经网络模型。选择日平均气压、日平均气温、日平均相对湿度、日降水量、日平均风速、前1日空气质量因子监测数据等因素作为模型输入变量,分别针对广东省所有监测站点和地市的空气质量因子(PM2.5、PM10、NO2、SO2、CO、O3、AQI)进行预测,结果表明,7个因子的地市平均相对误差值为16.15%~27.7%,地市相关系数为0.36~0.77,该模型在城市空气质量预测中具有良好的效果。
关键词:  神经网络  高层神经网络接口  空气质量  预测模型
DOI:
分类号:X831;TP183
文献标识码:B
基金项目:国家科技支撑计划基金资助项目(2014BAC21B03)
Application of Neural Network in Air Quality Forecast by Keras
XU Zhi guo
Guangdong Environmental Information Center, Guangzhou,Guangdong 510308, China
Abstract:
In this paper, a set of neural network system has been built for air quality prediction by python language. The bottom layer adopted Keras design and the neural network model was established based on tensorflow. The system has been applied to forecast air quality (PM2.5, PM10, NO2,SO2, CO, O3, AQI) in all monitoring stations and prefecture level cities of Guangdong Province, on the basis of taking the meteorological data such as daily mean pressure, daily mean temperature, daily mean relative humidity, daily precipitation, daily mean wind speed and previous days monitoring data of air quality as the input data. The results showed that the average mean relative error values of prefecture level cities were between 16.15% and 27.7%, and the average correlation coefficient values of prefectural level cities were between 36.41% and 76.65%. These results proved that the model is highly accessible and feasible in urban air quality prediction.
Key words:  Neural network  Keras  Air quality  Prediction model