Digital Water Utilities

AI, Data Platforms, and Smart Network Optimization for Water Infrastructure Management

De: Robert C. Brears

Narrado por: Virtual Voice

Water utilities face aging infrastructure, rising energy costs, tightening regulations, and climate-driven supply variability — all at once. The tools to address these pressures exist: AI-driven leak detection, predictive asset management, real-time hydraulic optimization, and continuous water quality surveillance. This book explains how they work, what they require, and what institutional conditions determine whether they deliver lasting operational change.
- Understand the layered architecture of a digital water utility, from sensor networks and SCADA systems to cloud platforms and machine learning pipelines
- Analyze the machine learning model types, training data requirements, and explainability frameworks relevant to water system AI deployment
- Discover how AI anomaly detection and acoustic sensing reduce non-revenue water losses and improve leak localization precision
- Evaluate predictive pipe failure models, risk scoring algorithms, and digital twin architectures for asset lifecycle management
- Examine real-time demand forecasting, pump scheduling optimization, and hydraulic model calibration methods that reduce energy costs and improve network control
- Interpret water quality monitoring technologies and AI-driven event detection frameworks within the EU Drinking Water Directive and US Safe Drinking Water Act compliance landscape
- Assess the cybersecurity threat landscape for water utility OT systems and the NIST, IEC 62443, and zero trust frameworks that address it
- Understand how regulatory models, green bond financing, workforce development, and governance reform shape the pace and scale of digital transformation
Written for water sector professionals, engineers, policy analysts, and researchers, this book provides the analytical depth needed to evaluate and guide AI investment in water infrastructure.