Prediction of atmospheric pollution by particulate matter using a neural network

被引:0
|
作者
Perez, P [1 ]
Trier, A [1 ]
Silva, C [1 ]
Montano, R [1 ]
机构
[1] Univ Santiago Chile, Dept Fis, Santiago 2, Chile
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Based on data of particulate matter pollutants (PM2.5) recorded hourly for six months in the city of Santiago, we show that after filtering the noise, feedforward neural networks may be used in order to predict particle concentration in the atmosphere several hours in advance. The predictions with the neural net are better than those using an ARIMA model and persistence.
引用
收藏
页码:1000 / 1003
页数:4
相关论文
共 50 条
  • [41] PREDICTION OF PARTICULATE MATTER CONTENT PM10 WITH ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION
    Stoyanov, N.
    Pandelova, A.
    Dzhudzhev, B.
    Georgiev, T. Z.
    Kalapchiiska, J.
    [J]. APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2023, 21 (06): : 5643 - 5655
  • [42] Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining
    Lu, Xiang
    Zhou, Wei
    Ly, Hai Bang
    Qi, Chongchong
    Nguyen, Thuy-Anh
    Nguyen, May Huu
    Huang, Jiandong
    Pham, Binh Thai
    [J]. APPLIED SOFT COMPUTING, 2023, 147
  • [43] Modeling atmospheric: Particulate matter
    Seigneur, C
    Pai, P
    Hopke, PK
    Grosjean, D
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 1999, 33 (03) : 80A - 86A
  • [44] Modeling atmospheric particulate matter
    Seigneur, Christian
    Pai, Prasad
    Hopke, Philip K.
    Grosjean, Daniel
    [J]. Environmental Science and Technology, 1999, 33 (03):
  • [45] Statistical modelling and prediction of atmospheric pollution by particulate material:: two nonparametric approaches
    Silva, C
    Pérez, P
    Trier, A
    [J]. ENVIRONMETRICS, 2001, 12 (02) : 147 - 159
  • [46] Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data
    Vakili, Masoud
    Sabbagh-Yazdi, Saeed Reza
    Khosrojerdi, Soheila
    Kalhor, Koosha
    [J]. JOURNAL OF CLEANER PRODUCTION, 2017, 141 : 1275 - 1285
  • [47] The Neural Network Modeling of Suspended Particulate Matter with Autoregressive Structure
    Aktan, Mehmet
    Bayraktar, Hanefi
    [J]. EKOLOJI, 2010, 19 (74): : 32 - 37
  • [48] The prediction of atmospheric concentrations of toluene using artificial neural network methods in Tehran
    Asadollahfardi, Gholamreza
    Aria, Shiva Homayoun
    Mehdinejad, Mahdi
    [J]. ADVANCES IN ENVIRONMENTAL RESEARCH-AN INTERNATIONAL JOURNAL, 2015, 4 (04): : 219 - 231
  • [49] Daily Prediction of PV Power Output Using Particulate Matter Parameter with Artificial Neural Networks
    Irmak, Erdal
    Yesilbudak, Mehmet
    Tasdemir, Oguz
    [J]. 2023 11TH INTERNATIONAL CONFERENCE ON SMART GRID, ICSMARTGRID, 2023,
  • [50] Soil Particulate Organic Matter (POM) Prediction in a Mountainous Watershed using Artificial Neural Networks
    Aghajani, M.
    Jalalian, A.
    Besalatpour, A. A.
    [J]. COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2015, 46 (08) : 925 - 938