  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.
