Air Quality Forcasting based on Gated Recurrent Long Short-Term Memory Model
With the continuous development of Chinese economy and the gradual acceleration of urbanization,it has caused tremen-dous damage to the environment.The effect of smog on peo-ple's lives has become more apparent.The bad air environ-ment seriously damages the physical and mental health of people.Predicting smog becomes very critical.Therefore,?how to accurately predict PM2.5 has become an important issue in recent years.The existing prediction models have limita-tions.They don't accurately capture the law between the concentration of haze and the factors affecting reality.It is difficult to accurately predict nonlinear smog data.One algorithm proposed in this paper is a two-layer model predic-tion algorithm based on Long Short Term Memory Neural Network and Gated Recurrent Unit(LSTM&GRU).This al-gorithm is an improvement and enhancement of the existing prediction method Long Short Term Memory.The Recur-rent Neural Network(RNN)is used to process important events in the series of prediction intervals and time delays.LSTM&GRU is a time-characteristic neural network algorith-m.The data of this experiment was obtained from Zhenqi Net(www.zhenqi.com).We selected daily smog data from 2014/1/1 to 2018/1/1 as a train and test dataset.The first 80%of the data was used for training and the rest for testing.The results of this experiment show that our model can play a better prediction.
Air pollution Forecasting LSTM GRU Air quality
Baowei Wang Weiwen Kong Hui Guan
School of Computer and Software Jiangsu Engineering Centre of Network Monitoring Jiangsu Collaborati School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment a
国际会议
2019国图灵大会(ACM Turing Celebration conference-China 2019 )
成都
英文
753-761
2019-05-17（万方平台首次上网日期，不代表论文的发表时间）