Air pollution forecasting with multivariate interval decomposition ensemble approach
- 13 October 2021
- journal article
- research article
- Published by Elsevier BV in Atmospheric Pollution Research
- Vol. 12 (12), 101230
- https://doi.org/10.1016/j.apr.2021.101230
Abstract
No abstract availableKeywords
Funding Information
- Fundamental Research Funds for the Central Universities (SK2021007)
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China (71988101, 72101197)
This publication has 49 references indexed in Scilit:
- Orthogonal incremental extreme learning machine for regression and multiclass classificationNeural Computing & Applications, 2014
- Equitability, mutual information, and the maximal information coefficientProceedings of the National Academy of Sciences of the United States of America, 2014
- Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatilityEnvironmental Modelling & Software, 2014
- Chemical composition and source identification of PM2.5 in the suburb of Shenzhen, ChinaAtmospheric Research, 2013
- Detecting Novel Associations in Large Data SetsScience, 2011
- Multivariate empirical mode decompositionProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2009
- Quality and performance of a PM10 daily forecasting modelAtmospheric Environment, 2008
- Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden NodesIEEE Transactions on Neural Networks, 2006
- Long Short-Term MemoryNeural Computation, 1997
- A Decision-Theoretic Approach to Interval EstimationJournal of the American Statistical Association, 1972