Forecasting Fine-Grained Air Quality Based On Deep Learning

In recent years,people have been paying more and more attention to air quality because it directly affects people’s health and daily life.Effective air quality prediction has become one of the hot research issues.In modern society,air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment.Among air pollutants,Particulate Matter(PM2.5)consists of suspended particles with a diameter equal to or less than 2.5 μm.Sources of PM2.5 can be coal-fired power generation,smoke,or dusts.These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body,which may further lead to other diseases such as asthma,lung cancer,or cardiovascular diseases.This thesis is facing many challenges,such as the instability of data sources and the variation of pollutant concentration along time series.Aiming at this problem,we propose an improved air quality prediction method based on the deep learning to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next hour.In this thesis we will address this problem by creating a new framework capable of predicting the PM2.5 concentration,for this purpose we will create a deep learning model,to monitor and estimate the PM2.5 concentration,Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)are combined and applied to forecast PM2.5 concentration.To compare the overall performance of each algorithm,three measurement indexes,Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Symmetric Mean Absolute Percentage Error(SMAPE)are applied to the experiments in this thesis.Compared with other machine learning methods,the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model is verified to be the highest in this thesis.For the CNN-LSTM model,its feasibility and practicability to forecast the PM2.5 concentration are also verified in this thesis.In the future,this study can also be applied to the prevention and control of PM2.5.