Automated State Estimation for Summarizing the Dynamics of Complex Urban Systems Using Representation Learning


  • Maira Alvi The University of Western Australia
  • Tim French The University of Western Australia
  • Philip Keymer The University of Queensland
  • Rachel Cardell-Oliver The University of Western Australia



Data Mining , Ecology and Environment , Smart Cities , Track: Innovative Inter-disciplinary AI Integration, Multidisciplinary Topics and Applications


Complex urban systems can be difficult to monitor, diagnose and manage because the complete states of such systems are only partially observable with sensors. State estimation techniques can be used to determine the underlying dynamic behavior of such complex systems with their highly non-linear processes and external time-variant influences. States can be estimated by clustering observed sensor readings. However, clustering performance degrades as the number of sensors and readings (i.e. feature dimension) increases. To address this problem, we propose a framework that learns a feature-centric lower dimensional representation of data for clustering to support analysis of system dynamics. We propose Unsupervised Feature Attention with Compact Representation (UFACR) to rank features contributing to a cluster assignment. These weighted features are then used to learn a reduced-dimension temporal representation of the data with a deep-learning model. The resulting low-dimensional representation can be effectively clustered into states. UFACR is evaluated on real-world and synthetic wastewater treatment plant data sets, and feature ranking outcomes were validated by Wastewater treatment domain experts. Our quantitative and qualitative experimental analyses demonstrate the effectiveness of UFACR for uncovering system dynamics in an automated and unsupervised manner to offer guidance to wastewater engineers to enhance industrial productivity and treatment efficiency.



How to Cite

Alvi, M., French, T., Keymer, P., & Cardell-Oliver, R. (2024). Automated State Estimation for Summarizing the Dynamics of Complex Urban Systems Using Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23020-23026.



IAAI Technical Track on Innovative Inter-disciplinary AI Integration