Research On Air Quality Prediction Based On Deep Learning

In recent years,as people's awareness of environmental protection continues to increase,the issue of air pollution has received more and more attention.Starting from the research background of air quality prediction,this thesis proposes an air quality prediction model and an air pollutant concentration prediction model based on deep learning,aiming at the problems of single structure and low prediction accuracy of air quality prediction model,and realizes prediction simulation software.This thesis mainly carries out the following research work:(1)According to the air quality data set of Xining City,Qinghai Province in the past five years,the hourly concentration values of six atmospheric pollutants:PM_2.5,PM₁₀,SO₂,NO₂,O₃,CO and their 24-hour moving average concentration values are used as attribute indicators,an air quality prediction model based on bidirectional gated recurrent unit and attention mechanism was researched and constructed to carry out short-term air quality prediction research in the future.Compared with the existing air quality prediction models,the air quality prediction model constructed in this thesis has achieved better results in terms of the mean absolute error,root mean square error and other evaluation indexes.(2)According to the air quality data set and atmospheric weather data set of Xining City,Qinghai Province,the combined method of convolutional neural network and bidirectional long-short term memory network based on attention mechanism was studied,and a hybrid prediction model of future short-term air pollutant concentration is constructed.Through the correlation analysis of air quality data and atmospheric meteorological data,different air pollutant concentration attributes and meteorological attributes are selected as indicators for predicting target concentrations to predict the future short-term concentration trends of four atmospheric pollutants:PM_2.5,PM₁₀,SO₂,and NO₂.Compared with the two models of Bi LSTM+Attention and LSTM+Attention,the air pollutant concentration prediction model used in this thesis is obviously superior to the other two models in terms of the mean absolute error and R-squared evaluation indexes.(3)The air quality prediction simulation software is designed to realize the main functions of data acquisition,data processing,data prediction and so on.This thesis is mainly based on time series data of air quality and time series data of atmospheric meteorology to carry out prediction simulation experiments.The experimental results show that the air quality prediction model and air pollutant concentration prediction model based on deep learning constructed in this thesis perform relatively well and effectively improve the data accuracy and stability of urban air quality prediction.This research work provides a certain reference value for the monitoring and forecasting of air environment quality.