[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
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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.

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来源期刊
Huanjing Kexue/Environmental Science
Huanjing Kexue/Environmental Science Environmental Science-Environmental Science (all)
CiteScore
4.40
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0.00%
发文量
15329
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