Open AccessAnalysis and Prediction of Air Quality based on LSTM Neural Network —— Take Beijing Temple of Heaven as an Example
Author(s) -
Huaiying Fang,
Lan Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1637/1/012126
Subject(s) - beijing , robustness (evolution) , computer science , air quality index , artificial neural network , heaven , time series , artificial intelligence , data mining , backpropagation , machine learning , meteorology , china , geography , archaeology , biochemistry , chemistry , gene
LSTM is an excellent variant model of RNN, which inherits the characteristics of most RNN models and solves the problem of gradient disappearance caused by a gradual reduction in the process of gradient backpropagation. LSTM is very suitable for processing data highly related to time series. This paper selects meteorological elements and air quality data of the temple of heaven in Beijing from 2016 to 2018, analyse the correlation between meteorological elements and air quality, selects index modeling, and designs an air quality prediction model based on LSTM neural network. It is concluded that the model has better accuracy and robustness. Finally, reasonable air pollution control measures are proposed to provide a scientific theoretical basis and new prediction methods for the prevention and control of air pollution.