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基于数据分解的大气污染物短期预测组合方法综述

李柚洁 赵顺昱 杨萍 王业林

李柚洁, 赵顺昱, 杨萍, 王业林. 基于数据分解的大气污染物短期预测组合方法综述[J]. 环境工程, 2023, 41(4): 213-224. doi: 10.13205/j.hjgc.202304029
引用本文: 李柚洁, 赵顺昱, 杨萍, 王业林. 基于数据分解的大气污染物短期预测组合方法综述[J]. 环境工程, 2023, 41(4): 213-224. doi: 10.13205/j.hjgc.202304029
LI Youjie, ZHAO Shunyu, YANG Ping, WANG Yelin. A REVIEW OF HYBRID FORECASTING METHODS FOR ATMOSPHERIC POLLUTANTS IN SHORT-TERM BASED ON DATA DECOMPOSITION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 213-224. doi: 10.13205/j.hjgc.202304029
Citation: LI Youjie, ZHAO Shunyu, YANG Ping, WANG Yelin. A REVIEW OF HYBRID FORECASTING METHODS FOR ATMOSPHERIC POLLUTANTS IN SHORT-TERM BASED ON DATA DECOMPOSITION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 213-224. doi: 10.13205/j.hjgc.202304029

基于数据分解的大气污染物短期预测组合方法综述

doi: 10.13205/j.hjgc.202304029
详细信息
    作者简介:

    李柚洁(1977-),女,博士,副教授,主要研究方向为计量经济学。liyoujie@kust.edu.cn

    通讯作者:

    王业林(1995-),男,研究生,主要研究方向为环境时序分析。wangyelin0@163.com

A REVIEW OF HYBRID FORECASTING METHODS FOR ATMOSPHERIC POLLUTANTS IN SHORT-TERM BASED ON DATA DECOMPOSITION

  • 摘要: 大气污染物的短期预测,对制定有效的大气环境治理措施和降低居民健康风险具有重要的实际参考价值。组合模型通过数据分解挖掘时间序列中蕴含的时频信息,可以进行精准且可靠的预测,已成为大气污染物短期预测的发展趋势。从时间尺度,将现有的大气污染物短期预测方法进行梳理,重点综述了基于小波分解、经验模态分解和变分模态分解的组合模型。随后,依据处理目的,将现有模型组合结构的优化方向归纳为数据降噪、二次分解、分量处理和误差修正,并对各结构的优缺点与适用范围进行总结。结果发现:4种组合结构并非普遍适用于所有预测情况,应根据数据特征等条件有选择性地使用。最后,总结了现存组合预测模型存在的问题,指出未来应从自适应组合结构、数据特征对性能影响和模型多性能平衡的角度开展相关研究。
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出版历程
  • 收稿日期:  2022-06-20
  • 网络出版日期:  2023-05-26
  • 刊出日期:  2023-04-01

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