Designing Sensor Networks and Locations on an Urban Sewershed Scale with Big Data Management and Analytics

Designing and implementing a sustainable Intelligent Water System at an urban sewershed level is more complex than simply identifying types of sensors and locations and managing Big Data. The proper design of an Intelligent Water System requires a “Systems Engineering” approach to understand the relationships between the natural, built, and social-economic systems. This project takes a deep dive into the literature, identifies relevant case studies, includes technology demonstrations, and demonstrates the use of the Digital Utility Maturity Assessment Model as a strategic planning tool. The final deliverable was the creation of a National Framework (iWISE). iWISE was thoroughly vetted, evaluated, and validated through extensive surveys and discussions with wastewater utility project partners and in discussions with utilities during regional conferences.

Through a project add-on effort, a separate report titled “AI Applications in the Water Sector” was developed. The artificial intelligence in the Water Sector (aiWATERS) report focuses on presenting fundamental understanding of Artificial Intelligence, the building blocks and the associated pillars and steps for implementation, and the organizational structure required to support the AI/ML solutions. aiWATERS was thoroughly vetted, evaluated, and validated through extensive surveys and discussions with water and wastewater utility project partners and in discussions with utilities during regional conferences. It is believed that AI/ML can one day eliminate the necessity for human workforce. While the path to implementing an Artificial Intelligence is unique to each utility, the steps for implementation are foundationally the same.

The aim of this report is to educate readers (especially, water sector utility manager level), about AI fundamental understanding, and step-by-step AI/ML implementation based on a layered framework, building blocks, and associated pillars that were developed to guide water utilities to take an agile AI/ML implementation approach. This is a myth, in addition to freeing up employees to do higher value work, this report presents a reasonable perspective to enable the Humans-in-the-Loop concept and trustworthy AI applications in the water sector. Co-sponsor: Metro Vancouver. Published in 2025.