A Visual Analytics System for Water Distribution System Optimization

TL;DR

This paper introduces a visual analytics system that leverages interpretable machine learning and linked visualizations to simplify the analysis of complex water distribution system simulations, aiding in the optimization of pump controls to reduce energy costs.

Contribution

The paper presents a novel visual analytics system integrating interpretable machine learning and visualization techniques specifically designed for optimizing water distribution system operations.

Findings

System reduces analysis time for WDS optimization

Helps identify optimal pump control schemes

Validated through a practical case study and expert reviews

Abstract

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.