A Visual Analytics System for Water Distribution System Optimization
This work addresses the challenge of identifying optimal pump controls for water distribution systems, which is incremental as it builds on existing simulation-based approaches with a new visualization tool.
The researchers tackled the problem of optimizing water distribution systems by developing a visual analytics system that incorporates interpretable machine learning and linked visualizations to help understand simulation input-output relationships, resulting in reduced analysis burden and assistance in determining optimal pump control schemes.
The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system's effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes.