Deep-learning based visualization tool for air pollution forecast

Nguyen, HAD; Le, HT; Barthelemy, X; Azzi, M; Duc, H; Jiang, N; Riley, M; Ha, QP

Deep-learning based visualization tool for air pollution forecast

Nguyen, HAD Le, HT Barthelemy, X Azzi, M Duc, H Jiang, N Riley, M Ha, QP

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Software, 2024, PP, (99), pp. 1-8
Issue Date:: 2024-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted versionAdobe PDF (1.42 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Nguyen, HAD
dc.contributor.author	Le, HT
dc.contributor.author	Barthelemy, X
dc.contributor.author	Azzi, M
dc.contributor.author	Duc, H
dc.contributor.author	Jiang, N
dc.contributor.author	Riley, M
dc.contributor.author	Ha, QP
dc.date.accessioned	2025-01-03T02:23:04Z
dc.date.available	2025-01-03T02:23:04Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Software, 2024, PP, (99), pp. 1-8
dc.identifier.issn	0740-7459
dc.identifier.issn	1937-4194
dc.identifier.uri	http://hdl.handle.net/10453/182915
dc.description.abstract	This paper presents a comprehensive visualization tool that integrates real-time observation and sensing data with various forecasting models, including both numerical and deep-learning approaches. The developed software framework efficiently manages data flow, configures forecasting models, and visualizes monitoring and prediction information. Additionally, the tool features a newly-developed WebApp dashboard, providing users with an interactive platform for real-time data access and decision-making. This web-based deep-learning framework is designed to enhance environmental monitoring and forecasting, providing a valuable tool for both the authority as well as the general public.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Software
dc.relation.isbasedon	10.1109/MS.2024.3496663
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0803 Computer Software, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Software Engineering
dc.subject.classification	4612 Software engineering
dc.title	Deep-learning based visualization tool for air pollution forecast
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0803 Computer Software
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	open_access	*
dc.date.updated	2025-01-03T02:23:03Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This paper presents a comprehensive visualization tool that integrates real-time observation and sensing data with various forecasting models, including both numerical and deep-learning approaches. The developed software framework efficiently manages data flow, configures forecasting models, and visualizes monitoring and prediction information. Additionally, the tool features a newly-developed WebApp dashboard, providing users with an interactive platform for real-time data access and decision-making. This web-based deep-learning framework is designed to enhance environmental monitoring and forecasting, providing a valuable tool for both the authority as well as the general public.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/182915