Predicting short-term PM2.5 concentrations at fine temporal resolutions using a multi-branch temporal graph convolutional neural network

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Geographical Information Science Pub Date : 2024-02-02 DOI:10.1080/13658816.2024.2310737

Qingfeng Guan, Jingyi Wang, Shuliang Ren, Huan Gao, Zhewei Liang, Junyi Wang, Yao Yao

引用次数: 0

Abstract

Predicting PM2.5 concentrations at an hourly temporal resolution in urban areas can provide key information for public health protection. The spatiotemporal dependency among monitoring stations and...

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利用多分支时间图卷积神经网络预测精细时间分辨率下的 PM2.5 短期浓度

以每小时的时间分辨率预测城市地区的 PM2.5 浓度可为公共健康保护提供关键信息。各监测站之间的时空依赖性和对空气质量的影响，都会影响PM2.5的预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.