Prediction of PM2.5 concentration based on the weighted RF-LSTM model

被引:2
作者
Ding, Weifu [1 ]
Sun, Huihui [1 ]
机构
[1] North Minzu Univ, Sch Math & Informat Sci, Yinchuan 750021, Ningxia, Peoples R China
关键词
Prediction; PM2.5; Random Forest; LSTM; Weighted RF-LSTM; Deep learning; AIR-POLLUTION; NEURAL-NETWORK; ROADSIDE; TERRAIN; CHINA;
D O I
10.1007/s12145-023-01111-7
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Accurate prediction of PM2.5 concentrations can provide a solid foundation for preventing and controlling air pollution. When the Long Short-Term Memory (LSTM) is applied to predict PM2.5 concentration, the influential factors strongly correlated with PM2.5 concentration are directly fed into the LSTM network. However, the influence of these factors on PM2.5 concentration is different. To address this issue, a weighted Random Forest (RF)-LSTM model was proposed to predict PM2.5 concentration for the next six hours in this study. This model first uses the RF to select the factors that are more important for predicting PM2.5 concentration and then uses a fully connected neural network to learn the weight value of each factor. Finally, the weighted data is fed into the LSTM network. The model is trained, validated, and tested using hourly air pollutant and meteorological data collected from four monitoring stations in Beijing, China, from November 1, 2019 to February 28, 2022. The prediction performance of the weighted RF-LSTM model was compared to the RF-LSTM and LSTM models. The results show that the RMSE and MAE of the weighted RF-LSTM model are the smallest, and the R2 is the largest for the next six hours' prediction of PM2.5 concentration at four stations. Compared to the LSTM model, the weighted RF-LSTM model decreases RMSE by 2.3%-5.3%, MAE by 5.6%-9.6%, and improves R2 by 2.0%-4.8%, showing that the weighted RF-LSTM model proposed in this study can achieve better prediction performance and has strong generalization ability.
引用
收藏
页码:3023 / 3037
页数:15
相关论文
共 48 条
  • [1] Air quality forecasting using arti ficial neural networks with real time dynamic error correction in highly polluted regions
    Agarwal, Shivang
    Sharma, Sumit
    Suresh, R.
    Rahman, Md H.
    Vranckx, Stijn
    Maiheu, Bino
    Blyth, Lisa
    Janssen, Stijn
    Gargava, Prashant
    Shukla, V. K.
    Batra, Sakshi
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 735
  • [2] A new method for prediction of air pollution based on intelligent computation
    Al-Janabi, Samaher
    Mohammad, Mustafa
    Al-Sultan, Ali
    [J]. SOFT COMPUTING, 2020, 24 (01) : 661 - 680
  • [3] Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system
    Byun, Daewon
    Schere, Kenneth L.
    [J]. APPLIED MECHANICS REVIEWS, 2006, 59 (1-6) : 51 - 77
  • [4] A review of artificial neural network models for ambient air pollution prediction
    Cabaneros, Sheen Mclean
    Calautit, John Kaiser
    Hughes, Ben Richard
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2019, 119 : 285 - 304
  • [5] An LSTM-based aggregated model for air pollution forecasting
    Chang, Yue-Shan
    Chiao, Hsin-Ta
    Abimannan, Satheesh
    Huang, Yo-Ping
    Tsai, Yi-Ting
    Lin, Kuan-Ming
    [J]. ATMOSPHERIC POLLUTION RESEARCH, 2020, 11 (08) : 1451 - 1463
  • [6] A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information
    Chen, Gongbo
    Li, Shanshan
    Knibbs, Luke D.
    Hamm, N. A. S.
    Cao, Wei
    Li, Tiantian
    Guo, Jianping
    Ren, Hongyan
    Abramson, Michael J.
    Guo, Yuming
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 636 : 52 - 60
  • [7] Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing
    Cheng, Xinghong
    Liu, Yuelin
    Xu, Xiangde
    You, Wei
    Zang, Zengliang
    Gao, Lina
    Chen, Yubao
    Su, Debin
    Yan, Peng
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 682 : 541 - 552
  • [8] Ding W., 2023, ENVIRON SCI POLLUT R, P1
  • [9] Prediction of PM2.5 Concentration in Ningxia Hui Autonomous Region Based on PCA-Attention-LSTM
    Ding, Weifu
    Zhu, Yaqian
    [J]. ATMOSPHERE, 2022, 13 (09)
  • [10] A hierarchical Bayesian model for the analysis of space-time air pollutant concentrations and an application to air pollution analysis in Northern China
    Ding, Weifu
    Leung, Yee
    Zhang, Jiangshe
    Fung, Tung
    [J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2021, 35 (11) : 2237 - 2271