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

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

1. 昆明理工大学管理与经济学院, 昆明 650093;
2. 广东工业大学生态环境与资源学院, 广州 510006

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

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- 文章访问数: 198
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- PDF下载量: 16
- 被引次数: 0
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

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

1. Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China;
2. School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China

摘要

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。
- 数据分解理论 /
- 大气污染物 /
- 短期预测 /
- 组合模型
Abstract: Atmospheric pollutants’ short-term prediction is of great significance to formulate effective control measures for the atmospheric environment and reduce the health risk on residents. Hybrid model can make accurate and reliable prediction by mining time-frequency information contained in time series via data decomposition, becoming the development trend of atmospheric pollutants’ short-term prediction. The existing short-term prediction models of atmospheric pollutants were sorted out from the time scale. Meanwhile, the hybrid models based on wavelet decomposition, empirical mode decomposition and variational mode decomposition were reviewed. Subsequently, according to the aims, the prediction structure of the hybrid model was summarized into data de-noising, secondary decomposition, component processing and error modification. The advantages, disadvantages, and application range of each structure were summarized. The results showed that the four hybrid structures were not universally applicable to all situations and should be used selectively according to data characteristics and other conditions. Finally, the issues of existing hybrid prediction models were summarized. It was pointed out that the future research should be carried out from the perspectives of the adaptive hybrid structure, the impact of data characteristics on performance and the balance of multi-performance of the model.
- data decomposition method /
- atmospheric pollutant /
- short-term forecasting /
- hybrid model

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