TY - GEN
T1 - AirNet
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
AU - Yu, Haomin
AU - Li, Qingyong
AU - Geng, Yangli Ao
AU - Zhang, Yingjun
AU - Wei, Zhi
N1 - Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2020
Y1 - 2020
N2 - Air pollution monitoring has attracted much attention in recent years. However, accurate and high-resolution monitoring of atmospheric pollution remains challenging. There are two types of devices for air pollution monitoring, i.e., static stations and mobile stations. Static stations can provide accurate pollution measurements but their spatial distribution is sparse because of their high expense. In contrast, mobile stations offer an effective solution for dense placement by utilizing low-cost air monitoring sensors, whereas their measurements are less accurate. In this work, we propose a data-driven model based on deep neural networks, referred to as AirNet, for calibrating low-cost air monitoring sensors. Unlike traditional methods, which treat the calibration task as a point-to-point regression problem, we model it as a sequence-to-point mapping problem by introducing historical data sequences from both a mobile station (to be calibrated) and the referred static station. Specifically, AirNet first extracts an observation trend feature of the mobile station and a reference trend feature of the static station via dual encoder neural networks. Then, a social-based guidance mechanism is designed to select periodic and adjacent features. Finally, the features are fused and fed into a decoder to obtain a calibrated measurement. We evaluate the proposed method on two real-world datasets and compare it with six baselines. The experimental results demonstrate that our method yields the best performance.
AB - Air pollution monitoring has attracted much attention in recent years. However, accurate and high-resolution monitoring of atmospheric pollution remains challenging. There are two types of devices for air pollution monitoring, i.e., static stations and mobile stations. Static stations can provide accurate pollution measurements but their spatial distribution is sparse because of their high expense. In contrast, mobile stations offer an effective solution for dense placement by utilizing low-cost air monitoring sensors, whereas their measurements are less accurate. In this work, we propose a data-driven model based on deep neural networks, referred to as AirNet, for calibrating low-cost air monitoring sensors. Unlike traditional methods, which treat the calibration task as a point-to-point regression problem, we model it as a sequence-to-point mapping problem by introducing historical data sequences from both a mobile station (to be calibrated) and the referred static station. Specifically, AirNet first extracts an observation trend feature of the mobile station and a reference trend feature of the static station via dual encoder neural networks. Then, a social-based guidance mechanism is designed to select periodic and adjacent features. Finally, the features are fused and fed into a decoder to obtain a calibrated measurement. We evaluate the proposed method on two real-world datasets and compare it with six baselines. The experimental results demonstrate that our method yields the best performance.
UR - http://www.scopus.com/inward/record.url?scp=85103819947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103819947&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85103819947
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 1129
EP - 1136
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI press
Y2 - 7 February 2020 through 12 February 2020
ER -