基于神经网络的城市大气雾霾污染短时预测方法研究  

Short-term Prediction of Urban Atmospheric Haze Pollution Based on Neural Network

作　　者：刘娜[1] Liu Na(Jilin Baishan Meteorological Bureau,Baishan 134300,China)

机构地区：[1]吉林省白山市气象局,吉林白山134300

出　　处：《环境科学与管理》2023年第7期99-104,共6页Environmental Science and Management

摘　　要：受到城市区域气候变化快、特征隐秘性强、可分析时间段短的影响,大气雾霾污染变化特征差异较大,短时预测适应性降低,由此,设计了基于神经网络的城市大气雾霾污染短时预测方法。构建神经网络预测结构,利用神经元的遗传特征,关联每一时间点下的有效雾霾特征,保证预测量信息特征范围的最大化,确定雾霾污染函数预测信息及其相关配置函数,进行大气雾霾短时预测层计算输出,获得短时预测结果。实验数据表明,提出方法具备减小预测误差,优化预测逻辑环境,提升预测速度的能力,保证适应性强,具有较高的推广与研究价值。Due to the rapid climate change,strong feature concealment,and short analytical time periods in urban areas,there are significant differences in the characteristics of atmospheric haze pollution changes,resulting in reduced adaptability for short-term prediction.Therefore,a neural network-based short-term prediction method for urban atmospheric haze pollution was designed.This paper constructed a neural network prediction structure by utilizing the genetic characteristics of neurons to associate effective haze features at each time point to ensure the maximization of the range of pre measured information feature.It determines the prediction information of haze pollution function and its related configuration functions,calculating and outputting the short-term prediction layer of atmospheric haze,and obtaining short-term prediction results.The experimental data shows that the proposed method has the ability to reduce prediction errors,optimize the prediction logic environment,improve prediction speed,ensure strong adaptability,offering high promotion and research value.

关 键 词：神经网络 雾霾污染 短时 预测 

分 类 号：X823[环境科学与工程—环境工程]