Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM 10 forecasting-Reference-Cited by-同舟云学术

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Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM 10 forecasting

Published:2018-03 Issue: Volume:201 Page:34-45
ISSN:0169-8095
Container-title:Atmospheric Research
language:en
Short-container-title:Atmospheric Research

Author:

Luo Hongyuan,Wang Deyun,Yue Chenqiang,Liu Yanling,Guo Haixiang

Funder

National Natural Science Foundation of China

Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry

Science Foundation of Mineral Resource Strategy and Policy Research Center, China University of Geosciences

Humanities and Social Sciences Research Foundation of Ministry of Education

Publisher

Elsevier BV

Subject

Atmospheric Science

Reference46 articles.

1. PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization;Antanasijevic;Sci. Total Environ.,2013

2. Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis;Chen;Atmos. Environ.,2013

3. An efficient gbest-guided Cuckoo Search algorithm for higher order two channel filter bank design. Swarm;Dhabal;Evol. Comput.,2017

4. PM2.5 analog forecast and Kalman filter post-processing for the Community Multi-scale Air Quality (CMAQ) model;Djalalova;Atmos. Environ.,2015

5. Variational mode decomposition;Dragomiretskiy;IEEE Trans. Signal Process.,2014

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