Water Infrastructure Data Visualization Framework

Abstract

The management of aging water infrastructure is a significant challenge for urban utilities, especially as the costs of repairs and replacements continue to escalate. This paper presents an innovative digital twin framework for Watertown City that integrates heterogeneous datasets-sensor readings, customer consumption data, and detailed pipe metadata-to offer a comprehensive visualization and predictive analytics platform. The application, implemented using Streamlit, provides interactive dashboards for water network exploration, usage analysis, pipe condition assessment, machine learning-based pipe-break prediction, and clustering-based network management. This paper details the data sources, preprocessing techniques, model development, visualization strategies, and experimental results. The paper also discusses insights gleaned from the analysis and outlines future enhancements to support real-time decision-making in water infrastructure management.