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Deep Multi-Task Learning Based Urban Air Quality Index Modelling

Authors:

Hanyu LongAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 1

Article No.: 2, Pages 1 - 17

https://doi.org/10.1145/3314389

Published: 29 March 2019 Publication History

Abstract

Obtaining comprehensive air quality information can help protect human health from air pollution. Existing spatially fine-grained estimation methods and forecasting methods have the following problems: 1) Only a part of data related to air quality is considered. 2) Features are defined and extracted artificially. 3) Due to the lack of training samples, they usually cannot achieve good generalization performance. Therefore, we propose a deep multi-task learning (MTL) based urban air quality index (AQI) modelling method (PANDA). On one hand, a variety of air quality-related urban big data (meteorology, traffic, factory air pollutant emission, point of interest (POI) distribution, road network distribution, etc.) are considered. Deep neural networks are used to learn the representations of these relevant spatial and sequential data, as well as to build the correlation between AQI and these representations. On the other hand, PANDA solves spatially fine-grained AQI level estimation task and AQI forecasting task jointly, which can leverage the commonalities and differences between these two tasks to improve generalization performance. We evaluate PANDA on the dataset of Hangzhou city. The experimental results show that our method can yield a better performance compared to the state-of-the-art methods.

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Cited By

Fan HLi HGu XRen Z(2024)Complex Real-Time Monitoring and Decision-Making Assistance System Based on Hybrid Forecasting Module and Social Network AnalysisSystems10.3390/systems1202003912:2(39)Online publication date: 24-Jan-2024
https://doi.org/10.3390/systems12020039
Chen LLong HXu JWu BZhou HTang XPeng L(2024)Deep Citywide Multisource Data Fusion-Based Air Quality EstimationIEEE Transactions on Cybernetics10.1109/TCYB.2023.324561854:1(111-122)Online publication date: Jan-2024
https://doi.org/10.1109/TCYB.2023.3245618
Liu XConcas FMotlagh NZaidan MFung PVarjonen SNiemi JTimonen HHussein TPetäjä TKulmala MNurmi PTarkoma S(2024)Estimating Black Carbon Levels With Proxy Variables and Low-Cost SensorsIEEE Internet of Things Journal10.1109/JIOT.2024.336197711:10(17577-17588)Online publication date: 15-May-2024
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Index Terms

Deep Multi-Task Learning Based Urban Air Quality Index Modelling
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

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Published In

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 1

March 2019

786 pages

EISSN:2474-9567

DOI:10.1145/3323054

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Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 August 2018

Published in IMWUT Volume 3, Issue 1

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Cited By

Fan HLi HGu XRen Z(2024)Complex Real-Time Monitoring and Decision-Making Assistance System Based on Hybrid Forecasting Module and Social Network AnalysisSystems10.3390/systems1202003912:2(39)Online publication date: 24-Jan-2024
https://doi.org/10.3390/systems12020039
Chen LLong HXu JWu BZhou HTang XPeng L(2024)Deep Citywide Multisource Data Fusion-Based Air Quality EstimationIEEE Transactions on Cybernetics10.1109/TCYB.2023.324561854:1(111-122)Online publication date: Jan-2024
https://doi.org/10.1109/TCYB.2023.3245618
Liu XConcas FMotlagh NZaidan MFung PVarjonen SNiemi JTimonen HHussein TPetäjä TKulmala MNurmi PTarkoma S(2024)Estimating Black Carbon Levels With Proxy Variables and Low-Cost SensorsIEEE Internet of Things Journal10.1109/JIOT.2024.336197711:10(17577-17588)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3361977
Mandal ASen R(2024)Medium-Term AQI Prediction in Selected Areas of Bangladesh Based on Bidirectional GRU Network ModelSN Computer Science10.1007/s42979-024-02971-65:5Online publication date: 8-Jun-2024
https://doi.org/10.1007/s42979-024-02971-6
Chen LXu JWu BHuang J(2023)Group-Aware Graph Neural Network for Nationwide City Air Quality ForecastingACM Transactions on Knowledge Discovery from Data10.1145/3631713Online publication date: 4-Nov-2023
https://doi.org/10.1145/3631713
Abdelfattah EAshkenazi Y(2023)Classifying Air Quality Using Machine Learning Models2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON59035.2023.10316018(0459-0463)Online publication date: 12-Oct-2023
https://doi.org/10.1109/UEMCON59035.2023.10316018
Tran-Van NThai HLe-Minh KLe K(2023)Towards Real-time Outdoor Air Quality Prediction Using a Hybrid Model Based on Internet of Things Devices2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)10.1109/COMNETSAT59769.2023.10420823(683-688)Online publication date: 23-Nov-2023
https://doi.org/10.1109/COMNETSAT59769.2023.10420823
Zhao YFan SXia KJia YWang LYang W(2023)ASTGC: Attention-based Spatio-temporal Fusion Graph Convolution Model for Fine-grained Air Quality AnalysisAir Quality, Atmosphere & Health10.1007/s11869-023-01369-216:9(1761-1775)Online publication date: 27-Jul-2023
https://doi.org/10.1007/s11869-023-01369-2
Hsieh HWu SKo CShei CYao ZChen Y(2022)Forecasting Fine-Grained Air Quality for Locations without Monitoring Stations Based on a Hybrid Predictor with Spatial-Temporal Attention Based NetworkApplied Sciences10.3390/app1209426812:9(4268)Online publication date: 23-Apr-2022
https://doi.org/10.3390/app12094268
Cheng YZhou ZThiele L(2022)iSprayProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172276:1(1-29)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517227
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