Managing critical water assets
Cost-efficient water-cycle services
Awareness & understanding
Increase food production
Welfare & healthy growth
Ensuring safe water
Access to timely water data
Reliable observation and forecasting
Sustainable water infrastructures
Efficient and competitive
Support, prevent and improve
Innovative, fast and low risk
Cost effective and scalable
Data analytics detect and solve drinking water network incidents.
Water leakages and unauthorised connections are the leading causes of Non-Revenue Water (NRW). Increasing water network efficiency is crucial to improve both water utility profitability and environmental sustainability.
ADASA has extensive experience implementing central systems that help facilitate fast and effective leak detection measures. Data analytics provide actionable insights that will improve operational awareness.
Water utilities have historically divided water networks into District Metered Areas (DMAs) to reduce NRW. A DMA is a specified area of water users within a water distribution network. DMAs are segregated from other users by using isolation valves; all the water flowing in and out is metered. Historically, contrasting water consumption with revenue has provided water utilities with estimations of each area’s NRW. Night consumption analysis - pressure increases at night due to demand drops, and as a result leakages increase - provided indications of which DMAs could potentially experience increased leakages and bursts. The effectiveness of these methods is not infallible, and many events go unnoticed.
Our solutions and consultancy services allow water utilities to effectively and quickly respond to incidents, and, most importantly, be proactive in preventing significant damage; detect, prioritise and schedule the repair of leaks before a pipe burst occurs.
At ADASA, we collect operational data from water networks and process it via an analytics engine that handles data validation, data gaps filling and anomaly detection. The analytics engine includes spatial models, time-series models and, recently, data models using Machine Learning techniques.
Recent improvements in data loggers, cloud computing, data analytics and the arrival of the Internet of Things (IoT) makes it possible to record, transfer, store and analyse large amounts of data. We process this in near real-time to provide applicable insights that improve operational awareness.
Advantages in this extra data include granular studies of DMA flows instead of assessing averages, more detailed customer meter data when Advanced Metering Infrastructure (AMI) is present, and the integration of data from acoustic sensors.
We accompany the transition from database-centric architectures to cloud computing to handle this ever-increasing amount of data. Move from reactive to proactive incident resolution and offer better operational awareness using data analytics with leakage detection systems.
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