An interval AQI combination prediction model based on multiple data decomposition and information aggregation operator-Reference-Cited by-同舟云学术

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An interval AQI combination prediction model based on multiple data decomposition and information aggregation operator

Published:2024-01-26 Issue:9 Volume:31 Page:14284-14302
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Wang Yixiang,Li Hao,Dai Xianchao,Zhou Ligang

Funder

National Natural Science Foundation of China

Natural Science Foundation for Distinguished Young Scholars of Anhui Province

Research Funding Project of Academic and technical leaders and reserve candidates in Anhui Province

Top Talent Academic Foundation for University Discipline of Anhui Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-024-32060-3.pdf

Reference36 articles.

1. Bates JM, Granger CWJ (1969) The combination of forecasts. J Oper Res Soc 20(4):451–468. https://doi.org/10.1057/jors.1969.103

2. Bliemel F (1973) Theil’s Forecast accuracy coefficient: a clarification. J Mark Res 10(4):444–446. https://doi.org/10.2307/3149394

3. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/tsp.2013.2288675

4. Fan JL, Ma X, Wu LF, Zhang FC, Yu X, Zeng WZ (2019) Light gradient boosting machine: an efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agric Water Manag 225:105758. https://doi.org/10.1016/j.agwat.2019.105758

5. Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmos Environ 32(14–15):2627–2636. https://doi.org/10.1016/s1352-2310(97)00447-0

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