Smart City Gnosys

Smart city article details

Title	Airex: Neural Network-Based Approach For Air Quality Inference In Unmonitored Cities
ID_Doc	7188
Authors	Sasaki Y.; Harada K.; Yamasaki S.; Onizuka M.
Year	2022
Published	Proceedings - IEEE International Conference on Mobile Data Management, 2022-June
DOI	http://dx.doi.org/10.1109/MDM55031.2022.00037
Abstract	Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to obtain air quality information continuously, but they do not cover all areas. Thus, there are numerous methods for spatially fine-grained air quality inference. Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities. In this paper, we first study the air quality inference in unmonitored cities. To accurately infer air quality in unmonitored cities, we propose a neural network-based approach AIREX. The novelty of AIREX is employing a mixture-of-experts approach, a machine learning technique based on the divide-and-conquer principle, to learn correlations of air quality between multiple cities. To further boost the performance, it employs attention mechanisms to compute the impacts of air quality inference from the monitored cities to the locations in the unmonitored city. Through experiments on a real-world air quality dataset, we show that AIREX achieves higher accuracy than state-of-the-art methods. © 2022 IEEE.
Author Keywords	Deep neural network; Internet of Things; Smart city; Spatio-temporal analysis

Similar Articles

Id	Similarity	Authors	Title	Published
53645	0.891	Essamlali I.; Nhaila H.; El Khaili M.	Supervised Machine Learning Approaches For Predicting Key Pollutants And For The Sustainable Enhancement Of Urban Air Quality: A Systematic Review	Sustainability (Switzerland), 16, 3 (2024)
42864	0.886	Varde A.S.; Pandey A.; Du X.	Prediction Tool On Fine Particle Pollutants And Air Quality For Environmental Engineering	SN Computer Science, 3, 3 (2022)
42245	0.882	Faydi M.; Zrelli A.; Ezzedine T.	Pm2.5 Prediction Using Deep Learning Models	17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023 - Proceedings (2023)
22959	0.88	Vanitha M.; Narasimhan D.	Empowering Urban Planning With Accurate Air Quality Index Prediction: Hybrid Learning Models For Smart Cities	Deep Learning and Blockchain Technology for Smart and Sustainable Cities (2025)
1343	0.88	Ghose B.; Rehena Z.; Anthopoulos L.	A Deep Learning Based Air Quality Prediction Technique Using Influencing Pollutants Of Neighboring Locations In Smart City	Journal of Universal Computer Science, 28, 8 (2022)
58757	0.876	Njaime M.; Abdallah F.; Snoussi H.; Akl J.; Chaaban K.; Omrani H.	Transfer Learning Based Solution For Air Quality Prediction In Smart Cities Using Multimodal Data	International Journal of Environmental Science and Technology, 22, 3 (2025)
23737	0.874	Shahbazi Z.; Shahbazi Z.; Nowaczyk S.	Enhancing Air Quality Forecasting Using Machine Learning Techniques	IEEE Access, 12 (2024)
10552	0.873	Neo E.X.; Hasikin K.; Lai K.W.; Mokhtar M.I.; Azizan M.M.; Hizaddin H.F.; Razak S.A.; Yanto	Artificial Intelligence-Assisted Air Quality Monitoring For Smart City Management	PeerJ Computer Science, 9 (2023)
33702	0.871	Alnowaiser K.; Alarfaj A.A.; Alabdulqader E.A.; Umer M.; Cascone L.; Alankar B.	Iot Based Smart Framework To Predict Air Quality In Congested Traffic Areas Using Sv-Cnn Ensemble And Knn Imputation Model	Computers and Electrical Engineering, 118 (2024)
49184	0.869	Garrido-Hidalgo C.; Solmaz G.; Jacobs T.; Roda-Sanchez L.	Smart Beestricts: Improving The Spatial Resolution Of Air-Quality Data In Madrid Through Transfer Learning	International Journal of Geographical Information Science (2025)