Artificial intelligence (AI) is making its mark on the water industry. It is powering intelligent operations using machine learning to optimize resource use and operational budgets for organizations, as well as delivering truly intelligent built water systems.
How critical is the telemetry initialization of your hydraulic model when assessing the impact of an incident? There is not a unique answer to that question, variations on boundary conditions will have different impacts depending on several variables, such as the amount of storage is available within the network, burst size and duration, incident location, etc.
Knowing which are the most critical pipes in the network with regards to burst events (for example in terms of customers affected by the potential failure) facilitates the planning of the maintenance activity, allowing for adequate resources allocation for critical assets.
Aging infrastructure combined with increasing customer expectations and demand growth are causing a real threat to our networks. Some assets in our networks could be considered critical: because of number of customers they serve, because of the type of customers (schools, hospitals, etc.), because of their age or because they affect resilience.