A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station

Yu Kang,Yang Cao,Zhenyi Xu,Jie Chen

doi:10.3390/rs13081600

Yu Kang, Yang Cao + Show 2 more

Open Access

https://doi.org/10.3390/rs13081600

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Journal: Remote sensing	Publication Date: Apr 20, 2021
Citations: 3	License type: CC BY 4.0

Affiliation: University of Science and Technology of China

Abstract

The location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our main objective was how to recommend optimal monitoring-station locations based on existing ones to maximize the accuracy of a air-quality inference model for inferring the air-quality distribution of an entire urban area. This task is challenging for the following main reasons: (1) air-quality distribution has spatiotemporal interactions and is affected by many complex external influential factors, such as weather and points of interest (POIs), and (2) how to effectively correlate the air-quality inference model with the monitoring station location recommendation model so that the recommended station can maximize the accuracy of the air-quality inference model. To solve the aforementioned challenges, we formulate the monitoring station location as an urban spatiotemporal graph (USTG) node recommendation problem in which each node represents a region with time-varying air-quality values. We design an effective air-quality inference model-based proposed high-order graph convolution (HGCNInf) that could capture the spatiotemporal interaction of air-quality distribution and could extract external influential factor features. Furthermore, HGCNInf can learn the correlation degree between the nodes in USTG that reflects the spatiotemporal changes in air quality. Based on the correlation degree, we design a greedy algorithm for minimizing information entropy (GMIE) that aims to mark the recommendation priority of unlabeled nodes according to the ability to improve the inference accuracy of HGCNInf through the node incremental learning method. Finally, we recommend the node with the highest priority as the new monitoring station location, which could bring about the greatest accuracy improvement to HGCNInf.

Highlights

In recent years, with economic growth, environmental problems have become increasingly prominent and air pollution is receiving unprecedented attention [1,2,3,4]
We design a greedy algorithm for minimizing information entropy based on the correlation degree between nodes in urban spatiotemporal graph (USTG), marking the recommendation priorities of unlabeled nodes according to the ability to improve the inference model accuracies using the incremental learning method to complete the monitoring-equipment station location recommendation
We designed a greedy algorithm for minimizing information entropy (GMIE) based on the correlation degree between nodes in USTG, marking the recommendation priorities of unlabeled nodes according to the ability to improve the inference accuracies of HGCNInf through the node incremental learning method to complete the monitoring station location recommendation

Summary

Introduction

With economic growth, environmental problems have become increasingly prominent and air pollution is receiving unprecedented attention [1,2,3,4]. The air-quality index (AQI) provides a number used by government agencies to communicate to the public how polluted the air is currently. As the AQI increases, an increasingly large percentage of the population is likely to experience increasingly severe adverse health effects [5,6,7]. To compute this AQI, an air-pollutant concentration from a monitor or model is required, such as carbon monoxide (CO), carbon dioxide (CO2 ), hydrocarbons (HC), nitrogen oxides (NOx ), solid particulate matter (PM2.5 and PM10 ), etc. In order to reflect the air quality and its development trends in a timely and accurate manner, we need accurate air-quality-monitoring equipment. In order to reflect the air quality and its development trends in a timely and accurate manner, we need accurate air-quality-monitoring equipment. 4.0/).

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