WC138 SeptOct 2024 - Flipbook - Page 35
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“The responsibility for providing safe water for consumption and cost-effective
water management to protect sensitive ecosystems belongs with human
professionals, including operators, engineers, and utility management.”
improvement over deterministic forecasts and static
safety factors.”
Oerther offers a second example: operational
support. He argues that the effort to link available
sensor data, current design schematics, and operator
experience creates a single framework for process
optimization and operational efficiency (especially in
energy usage). Cybersecurity, including the reliability
of sensor data and the protection of mission critical
information, is a major challenge for operational
support of inherently vulnerable infrastructure.
Oerther’s third example is evidence-informed
design: Design links current operation with future
forecasts, and digital twins often are used to provide
in silico systems for experimentation and stress testing of scenarios. He said he believes that water sector
models offer testable frameworks to gather stakeholder input and consider project costs.
WAT E R C A N A D A . N E T
The uses of AI in the water sector include improvements to current approaches to digital water, as well
as new opportunities such as anomaly detection and
offering alternatives for consideration by humans.
The responsibility for providing safe water for consumption and cost-effective water management to
protect sensitive ecosystems belongs with human professionals, including operators, engineers, and utility
management. The role for AI in the water sector is to
support the work of human decision-makers.
One important research article was published
by Prof. Neelke Doorn, “AI in the Water Domain:
Opportunities for Responsible Use” in Science of
The Total Environment (Vol. 755, Part 1, February
10). Doorn identified four categories where AI is
being applied: modeling, prediction and forecasting,
decision support and operational management, and
optimization.
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