Time-series prediction model of PM2.5 concentration based on LSTM neural network

Abstract

LSTM (long-short term memory neural network) is a neural network suitable for simulating and predicting time series changes. This paper designs a PM2.5 concentration prediction model based on the LSTM neural network. Use Python language programming implementation under the Tensor Flow framework, training the model using real weather data from 2015-01-01∼2019-01-01, and the obtained model can accurately predict the concentration change of future PM2.5. RMSE and DC were used as evaluation indexes to compare the LSTM neural network model with the traditional BP (Back Propagation) algorithm model. It turns out, LSTM model has the characteristics of high precision and stable prediction effect, proved that the time series prediction ability of the LSTM model is more superior. As an effective method for predicting PM2.5 concentration data, it can provide the basis for predicting pollutants.