Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 184-189.doi: 10.11896/jsjkx.200600090

• Big Data & Data Science • Previous Articles     Next Articles

A Kind of High-precision LSTM-FC Atmospheric Contaminant Concentrations Forecasting Model

LIU Meng-yang1,2, WU Li-juan1,3, LIANG Hui1,3, DUAN Xu-lei1,3, LIU Shang-qing1,3, GAO Yi-bo1,3   

  1. 1 Tianjin Intelligent Tech Institute of CASIA Tianjin,Tianjin 300300,China
    2 School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China
    3 Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:LIU Meng-yang,born in 1998,undergraduate.His main rersearch interests include machine learning,deep learning and high performance computing.
    WU Li-juan,born in 1987,postgradua-te,engineer.Her main research inte-rests include intelligent information processing,prediction of air pollutants and big data analysis.
  • Supported by:
    Science and Technology Major Project of Cross Border Integration and Innovation of Internet of Tianjin and Big Data Analysis Platform for Air Pollutant Monitoring(18ZXRHSF00250).

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Abstract: Atmospheric contamination can pose a severe threat to the health of people and incur kinds of diseases,thus,forecasting the concentration of atmospheric contaminant can be of great significance for instructing the atmospheric pollution control.To solve the issue,we propose a kind of mixed forecasting model based on LSTM and full connected neural network,and we introduce the training strategyof data bucket,which can address the issue that the long interval between training data and forecasting sample.Our model has a high performance on both versatility and precision,we fully combine the advantages of LSTM and full connected together and achieve high precision forecasting with varieties of contaminants.Finally,we take an example of forecasting of Tianjin to validate its strength and the results show that our model can achieve R2>0.90,MSE＜0.15performance for all six kinds of pollutant.It shows that LSTM-FC Model has its great strength for atmospheric contaminant concentrations task.

Key words: Atmospheric contaminant forecasting, Full connected neural network, Hybrid neural network model, Long short-term memory neural network, Multi-dimension feature fusion

CLC Number: 

  • TPX513
[1] JIANG B.Review of epidemiological evidences for association between air pollution and stroke[J].Chinese General Practice,2018,21(18):2156-2162.
[2] BROOK R D,RAJAGOPALAN S,POPE C A,et al.Particulate matter air pollution and cardiovascular disease an update to the scientific statement from the american heart association [J].Circulation, 2010, 121(21):2331-2378.
[3] LIPSETT M J,OSTRO B D,REYNOLDS P,et al.Long-term exposure to air pollution and cardiorespiratory disease in the California teachers study cohort[J].American Journal of Respiratory and Critical Care Medicine, 2011, 184(7):828-835.
[4] HUANG S X. Air pollution and control:Past, present and future[J].Chinese Science Bulletin,2018,63(10):895-919.
[5] SCHERE K L.U.S.EPA MODELS-3/CMAQ - STATUS AND APPLICATIONS[R].Presented at US/German Ozone/Fine Particle Science and Environmental Chamber Workshop, Riverside, CA, 1999.
[6] BAI S N,SHEN X L. PM2.5 PREDICTION BASED ONLSTM RECURRENT NEURAL NETWORK [J].Computer Applications and Software ,2019,36(1):73-76,110.
[7] HAN W, WU Y L, REN F.The Prediction of Air Pollutants Based on Full Connection and LSTM Neural Network [J].Geomatics World ,2018,25(3):34-40.
[8] MENG Q.Air Quality Classification Prediction Based on Random Forest Model[J].J Chongqing Technol Business Univ.( Nat Sci Ed) 2018,179(3):33-37.
[9] DU X, F J Y, L S Q, S W.PM2.5 concentration prediction model based on random forest regression analysis[J].Telecommunication Science 2017,33(7):66-75.
[10] KULKARNI G E, MULEY A A, DESHMUKH N K, et al.Autoregressive integrated moving average time series model for forecasting air pollution in Nanded city, Maharashtra, India[J].Modeling Earth Systems & Environment, 2018, 4(4):1435-1444.
[11] PENG S J, SHEN J C, ZHU X.Forecast of PM2.5 Based on the ARIMA Model[J].Safety and Environmental Engineering ,2014,21(6):125-128.
[12] HOCHREITER S, JÜRGEN S.Long short-term memory[J].Neural Computation, 1997, 9(8) :1735-1780.
[13] SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[14] KINGMA D,BA J.Adam:A Method for Stochastic Optimization[J].Computer Science, 2014.
[15] ZHAI B, CHEN J.Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China[J].Science of the Total Environment, 2018,635:644-658.
[16] GAN K,SUN S,WANG S,et al.A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5,concentration[J].Atmospheric Pollution Research,2018:S1309104217306190.
[17] WANG P,ZHANG H,QIN Z,et al.A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting[J].Atmospheric Pollution Research,2017:S1309104216302616.
[18] NG K Y,AWANG N.Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia[J].Environmental Monitoring and Assessment,2018,190(2):63.
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