基于深度学习和适当时空相关性分析的新型颗粒物(PM2.5)预报方法

IF 1.8 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Unjin Pak , YongBom Son , Kwangho Kim , JangHak Kim , MyongJun Jang , KyongJin Kim , GumRyong Pak
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引用次数: 0

摘要

由于 PM2.5(空气动力学直径≤2.5 μm的颗粒物)造成的空气污染严重威胁人类健康,准确预报城市地区 PM2.5 浓度是减少和消除 PM2.5 对人类造成危害的先决条件之一。在本研究中,我们分析了与空气污染预报相关的目标参数和观测参数之间的时空相关性,并提出了一种卷积神经网络(CNN)和长短期记忆(LSTM)模型(又称 PM 预测器),用于北京地区次日 PM2.5 日均浓度的预报。对所提出的时空相关性进行了分析,以有效估计互信息,不仅考虑两个空间之间的变化程度是否相似,还考虑变化程度是否存在显著差异,从而生成时空特征向量。CNN 有效提取了与 PM 2.5 相关的潜在空气质量和气象输入数据的固有特征,而 LSTM 则提供了时间序列数据中的历史信息,因此,与多层感知器(MLP)和 LSTM 模型相比,构建的新型 PM 预测器在整体预测方面的性能有了显著提高。数据集采用了北京及周边四个城市监测站 2015 年 1 月 1 日至 2017 年 12 月 31 日的空气质量和气象数据。预报结果表明,所提出的 PM 预测模型在总体预报中优于其他模型,而 LSTM 在季节预报中优于 PM 预测模型,但略有差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel particulate matter (PM2.5) forecasting method based on deep learning with suitable spatiotemporal correlation analysis

Since air pollution caused by PM 2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) is a serious threat to human health, the accurate forecasting of PM 2.5 concentration in metropolitan areas is one of the prior conditions to reduce and eliminate the harmful impacts on human beings produced by PM2.5. In this study, we analyzed the spatiotemporal correlations between target and observation parameters relevant to air pollution forecasting and proposed a convolutional neural network (CNN) and long short-term memory (LSTM) model (also called PM predictor) for next day's daily average PM 2.5 concentration forecasting in Beijing. The proposed spatiotemporal correlations were analyzed for efficient estimation of mutual information, not only if the degrees of variations between the two spaces under consideration are similar, but also if the degrees of variations are significantly different, thereby generating a spatiotemporal feature vector. CNN provided an efficient extraction of inherent features for latent air quality and meteorological input data relevant to PM 2.5, and LSTM delivered the historical information in the time series data, thus a novel PM predictor with remarkably improved performance was constructed, compared with multi-layer perceptron (MLP) and LSTM model in overall forecasting. The air quality and meteorological data from the monitoring stations in Beijing and four surrounding cities from January 1, 2015 to December 31, 2017 were adopted as dataset. The forecasting results suggest that the proposed PM predictor is superior to other models in overall forecasting, while LSTM is better than PM predictor with slight difference in seasonal forecasting.

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来源期刊
Journal of Atmospheric and Solar-Terrestrial Physics
Journal of Atmospheric and Solar-Terrestrial Physics 地学-地球化学与地球物理
CiteScore
4.10
自引率
5.30%
发文量
95
审稿时长
6 months
期刊介绍: The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is an international journal concerned with the inter-disciplinary science of the Earth''s atmospheric and space environment, especially the highly varied and highly variable physical phenomena that occur in this natural laboratory and the processes that couple them. The journal covers the physical processes operating in the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, magnetosphere, the Sun, interplanetary medium, and heliosphere. Phenomena occurring in other "spheres", solar influences on climate, and supporting laboratory measurements are also considered. The journal deals especially with the coupling between the different regions. Solar flares, coronal mass ejections, and other energetic events on the Sun create interesting and important perturbations in the near-Earth space environment. The physics of such "space weather" is central to the Journal of Atmospheric and Solar-Terrestrial Physics and the journal welcomes papers that lead in the direction of a predictive understanding of the coupled system. Regarding the upper atmosphere, the subjects of aeronomy, geomagnetism and geoelectricity, auroral phenomena, radio wave propagation, and plasma instabilities, are examples within the broad field of solar-terrestrial physics which emphasise the energy exchange between the solar wind, the magnetospheric and ionospheric plasmas, and the neutral gas. In the lower atmosphere, topics covered range from mesoscale to global scale dynamics, to atmospheric electricity, lightning and its effects, and to anthropogenic changes.
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