Data analytics for sewer rising main failures

Project Member(s): Wang, Y., Guo, T., Chen, F.

Funding or Partner Organisation: Sydney Water Corporation
Sydney Water Corporation

Start year: 2020

Summary: Investigation of sewer rising main failures to understand the causes and enable improved management of future risk through data analytics technology.

Publications:

Li, B, Guo, T, Zhu, X, Wang, Y & Chen, F 2023, 'ConGCN: Factorized Graph Convolutional Networks for Consensus Recommendation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 369-386.
View/Download from: Publisher's site

FOR Codes: Pattern Recognition and Data Mining, Infrastructure Engineering and Asset Management, Water Recycling Services (incl. Sewage and Greywater), Data management and data science, Machine learning, Computer vision and multimedia computation

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.