Incorporating ubiquitous data in air pollution forecast model for performance improvement

Project Member(s): Ha, Q.

Funding or Partner Organisation: NSW Department of Planning, Industry and Environment
NSW Department of Planning, Industry and Environment

Start year: 2022

Summary: This proposal presents the next step in the collaboration between UTS and DPIE -Atmospheric Research to focus on the following deliverables: (i) Model configuration analysis, involving an optimisation process for improving versatility, (ii) Uncertainty management and reliability enhancement with the developed LSTM-BNN technique, taking LWSN and CMAQ data into account, and (iii) Implementation of the developed algorithms into the prototype WebApp demonstrator.

Publications:

Nguyen, HAD, Le, TH, Ha, QP & Azzi, M 1970, 'Deep learning for construction emission monitoring with low-cost sensor network', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 40th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC).
View/Download from: Publisher's site

FOR Codes: Air quality, atmosphere and weather, Automation engineering, Network engineering

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.