Optimizing water distribution both from an energy-saving perspective and a quality-of-service perspective is challenging since it involves a complex system with many nodes, hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution.

The proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. It could be applied to any complex network with historically labeled data. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. 

Materials and Methods

The water source in a water supply system is generally a lake, a river or an underground aquifer. Water then flows through water treatment facilities to purify and deliver to all the demand points. This defines, especially if the area to be covered by the water main is large, a complex network constituted by many interconnected elements and sub-systems with different functions.

Different criteria and parameters may determine the quality of service; for example, the water quality can be assessed. Another relevant objective of the water main system is to ensure that the water reaches every area of the network under satisfactory pressure. To assess this and to optimize it, pressure sensors are distributed in the network, and an optimal pressure range is defined for each of the monitored points.

Detecting abnormal flows

AI can also detect abnormal water flow and any pipes burst. As part of the research, an artificial neural network model, a mixture density network, was trained using a continually updated historical database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared the latest observed flow values with predicted flows over time windows such that alerts are generated in the event of abnormal flow conditions. 

The fuzzy inference system provides confidence intervals associated with each detection from the probability density functions of predicted flows. These confidence values provide useful information for filtering and ranking alerts. An accurate estimate of abnormal flow magnitude is produced to further aid in ranking alerts.

A water supply system in the U.K. was used for a case study with near real-time flow data provided by a general packet radio service. The online burst alert system was constructed to operate alongside an existing flat-line alarm system and continuously analyze a set of 144 DMAs every hour. The new system identified a number of events, and alerts were raised before their detection in the control room, either through flat-line alarms or customer contacts.

Conclusion

The use of AI, in general, and in water supply, in particular, has several policy implications for improving the performance of water utilities and the quality of service delivery: Ethics and governance deal with the protection of personal and financial data from consumers, and technical and financial data from water utilities. Regulation deals with benchmarking since unaccounted-for-water (UFW) is one of the key operational parameters to determine the efficiency of a water utility in reducing both physical losses (e.g., water leaks and pipe bursts) and commercial losses (such as illegal connection and metering errors). Technical policies deal with line ministries and water associations to update the national and water “Code of Practice” and to guide water utilities in their digital transformation. Financial policies address short-term CAPEX requirements to finance smart water utilities with new financing instruments.

The digital transformation of water supply supports the Sustainable Development Goals (SDG), especially SDG 6: Ensure availability and sustainable management of water and sanitation for all, and SDG 13: Climate Action, by taking urgent actions to promote climate-related investments to combat climate change impacts.