基于门控循环单元神经网络的PM_(2.5)浓度预测

Prediction of PM_(2.5) concentration based on gated recurrent unit neural network

文章首先针对延安市市监测站单站点观测数据与PM_(2.5)的关系,从中抽取了影响PM_(2.5)较为明显的14组特征数据。依据所抽取的数据,利用LSTM深度神经网络的一种变体GRU建立了未来数小时的PM_(2.5)浓度预测模型,通过仿真实验,该模型对PM_(2.5)预测有较高的一致性,可以较好地满足日常预测业务需求。

This paper firstly analyzes the relationship between single-site observation data and PM2.5 of Yan’an City Monitoring Station,and extracts 14 sets of characteristic data that affect PM2.5.Based on the extracted data, the GRU, a variant of the LSTM deep neural network, is used to establish a PM2.5 concentration prediction model for the next few hours. Through simulation experiments, the model has a high consistency for PM2.5 prediction, and goodly meet the daily forecast business needs.