[基于 KZ 滤波技术和 LSTM 的上海臭氧预测模型]。

Q2 Environmental Science
Ling-Xia Wu, Jun-Lin An, Dan Jin
{"title":"[基于 KZ 滤波技术和 LSTM 的上海臭氧预测模型]。","authors":"Ling-Xia Wu, Jun-Lin An, Dan Jin","doi":"10.13227/j.hjkx.202311150","DOIUrl":null,"url":null,"abstract":"<p><p>In this study, a Kolmogorov-Zurbenko (KZ) filter was proposed to decompose the original ozone (O<sub>3</sub>) sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy (mRMR) feature selection technique was combined with the support vector regression (SVR) approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network (LSTM) was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan (urban area), Pudong-Chuansha (suburban area), and Dianshan Lake (suburban area) in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O<sub>3</sub> baseline and short-term components, as chosen by feature screening. The <i>R</i><sup>2</sup> values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m<sup>-3</sup>, respectively. These findings suggest that decomposing the original O<sub>3</sub> sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the <i>R</i><sup>2</sup> and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 10","pages":"5729-5739"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Predictive Model for O<sub>3</sub> in Shanghai Based on the KZ Filtering Technique and LSTM].\",\"authors\":\"Ling-Xia Wu, Jun-Lin An, Dan Jin\",\"doi\":\"10.13227/j.hjkx.202311150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this study, a Kolmogorov-Zurbenko (KZ) filter was proposed to decompose the original ozone (O<sub>3</sub>) sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy (mRMR) feature selection technique was combined with the support vector regression (SVR) approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network (LSTM) was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan (urban area), Pudong-Chuansha (suburban area), and Dianshan Lake (suburban area) in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O<sub>3</sub> baseline and short-term components, as chosen by feature screening. The <i>R</i><sup>2</sup> values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m<sup>-3</sup>, respectively. These findings suggest that decomposing the original O<sub>3</sub> sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the <i>R</i><sup>2</sup> and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.</p>\",\"PeriodicalId\":35937,\"journal\":{\"name\":\"环境科学\",\"volume\":\"45 10\",\"pages\":\"5729-5739\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"环境科学\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.13227/j.hjkx.202311150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202311150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

摘要

在这项研究中,提出了一种柯尔莫哥洛夫-祖尔宾科(KZ)滤波器来分解原始臭氧(O3)KZ)滤波器对原始臭氧(O3)序列进行分解,以提高臭氧长期序列预测的精度,并筛选出相关的气象特征。此外,增强的最大最小冗余(mRMR)特征选择技术与支持向量回归(SVR)方法来选择最具启发性的气象特征。随后,在 2023 年 5 月至 8 月臭氧浓度较高期间,利用长短期记忆网络(LSTM)对上海静安(郊区)、浦东川沙(郊区)和淀山湖(郊区)监测站的臭氧高浓度时段进行评估和预测。上海淀山湖(郊区)。结果表明,通过特征筛选,气压、温度、湿度、边界层高度和风向是 O3 基线和短期成分的最佳组合。静安站、浦东-川沙站和淀山湖站的 R2 值分别为 0.86、0.83 和 0.85。RMSE 值分别为 18.26、18.74 和 20.02 μg-m-3。这些结果表明,分解原始 O3 序列提高了臭氧浓度的预测精度。此外,从每个监测站的 R2 和 RMSE 值可以看出,特征筛选保持了模型的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Predictive Model for O3 in Shanghai Based on the KZ Filtering Technique and LSTM].

In this study, a Kolmogorov-Zurbenko (KZ) filter was proposed to decompose the original ozone (O3) sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy (mRMR) feature selection technique was combined with the support vector regression (SVR) approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network (LSTM) was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan (urban area), Pudong-Chuansha (suburban area), and Dianshan Lake (suburban area) in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O3 baseline and short-term components, as chosen by feature screening. The R2 values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m-3, respectively. These findings suggest that decomposing the original O3 sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the R2 and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
期刊介绍:
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信