Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions-Reference-Cited by-同舟云学术

Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Published:2016-05 Issue:3 Volume:7 Page:557-566
ISSN:1309-1042
Container-title:Atmospheric Pollution Research
language:en
Short-container-title:Atmospheric Pollution Research

Author:

Bai Yun,Li Yong,Wang Xiaoxue,Xie Jingjing,Li Chuan

Publisher

Elsevier BV

Subject

Atmospheric Science,Pollution,Waste Management and Disposal

Reference47 articles.

1. PM10 forecasting using soft computing techniques;Ababneh;Res. J. Appl. Sci. Eng. Technol.,2014

2. Forecasting human exposure to PM10 at the national level using an artificial neural network approach;Antanasijević;J. Chemom.,2013

3. A multi-scale relevance vector regression approach for daily urban water demand forecasting;Bai;J. Hydrol.,2014

4. Artificial neural network model to forecast the concentration of pollutants over Delhi: skill assessment of learning rules;Chaudhuri;Asian J. Water Environ. Pollut.,2012

5. Seasonal modeling of PM2.5 in California's San Joaquin Valley;Chen;Atmos. Environ.,2014

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