利用异构图神经网络进行全国空气污染预测

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-12-14 DOI:10.1145/3637492
Fernando Terroso-Saenz, Juan Morales-García, Andres Muñoz
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引用次数: 0

摘要

如今,空气污染是大多数城市环境中最相关的环境问题之一。由于在预测某些污染水平的操作方面的效用,在过去几年中已经提出了几种基于图神经网络(GNN)的预测器。大多数解决方案通常根据站点之间的空间距离编码站点之间的关系,但是当涉及到捕获其他基于空间和特征的上下文因素时,它们就失败了。此外,他们假设一个均匀的设置,所有的站能够捕获相同的污染物。然而,大尺度设置往往包括不同类型的台站,每一个都有不同的测量能力。因此,本文介绍了一种新的GNN框架,能够捕捉到站点之间与其位置的土地利用及其主要污染源有关的相似性。此外,我们定义了一种方法来处理GNN体系结构顶部的异构设置。最后,该提案已经在西班牙全国空气污染数据集上进行了测试,结果非常有希望。
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Nationwide Air Pollution Forecasting with Heterogeneous Graph Neural Networks

Nowadays, air pollution is one of the most relevant environmental problems in most urban settings. Due to the utility in operational terms of anticipating certain pollution levels, several predictors based on Graph Neural Networks (GNN) have been proposed for the last years. Most of these solutions usually encode the relationships among stations in terms of their spatial distance, but they fail when it comes to capture other spatial and feature-based contextual factors. Besides, they assume a homogeneous setting where all the stations are able to capture the same pollutants. However, large-scale settings frequently comprise different types of stations, each one with different measurement capabilities. For that reason, the present paper introduces a novel GNN framework able to capture the similarities among stations related to the land use of their locations and their primary source of pollution. Furthermore, we define a methodology to deal with heterogeneous settings on the top of the GNN architecture. Finally, the proposal has been tested with a nation-wide Spanish air-pollution dataset with very promising results.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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