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Explore the latest innovations, case studies, and AI-driven solutions for sustainable and efficient water utilities.
In the world of IoT water monitoring, the shift from reactive, ad hoc maintenance to proactive, predictive analytics is revolutionizing water resource management. By leveraging cutting-edge technologies such as
Non-Revenue Water (NRW)—water lost before it reaches consumers due to leaks, theft, or metering inaccuracies—is a significant challenge for water utilities worldwide. Generative AI models,
Water utilities worldwide face the persistent challenge of Non-Revenue Water (NRW)—water that is lost before it reaches consumers due to leaks, theft, or metering inaccuracies.
Water utilities worldwide face a convergence of pressures: ageing infrastructure, tightening regulatory requirements, rising energy costs, and the urgent need to reduce losses. The numbers are stark: approximately 30% of all treated drinking water is lost to leaks globally, according to the International Water Association (IWA). Manual meter reading programmes cost utilities between €5 and €12 per meter per year in labour alone. And reactive maintenance — fixing infrastructure only after it fails — costs 3 to 5 times more than predictive approaches that intervene before failure occurs. Artificial intelligence, applied directly to the real-time IoT data already flowing from smart meters, pressure loggers, and flow sensors, is the key that unlocks a fundamentally more efficient and resilient water operation.
ThingsLog embeds machine learning models directly into its cloud platform, operating on the continuous stream of sensor data collected by ThingsLog data loggers deployed across your network. No third-party AI tool is required — the intelligence is built into the platform you already use.
ML models trained on historical pressure and flow patterns establish a dynamic baseline for each district metered area (DMA). When readings deviate from that baseline — a pressure dip at 03:00, an unusually elevated minimum night flow — the system flags the anomaly and classifies it against known signatures of leaks, burst pipes, or illegal connections. The affected DMA zones are pinpointed automatically, reducing the time field crews spend searching from days to hours.
Time-series forecasting algorithms analyse historical consumption data alongside weather, day-of-week, and seasonal variables to predict daily and peak demand. This allows operations teams to pre-position reservoir levels, adjust pumping schedules proactively, and avoid reactive pressure adjustments that stress ageing pipework.
Excessive pressure accelerates pipe degradation and increases leak rates. ThingsLog AI analyses pressure sensor data across the network topology and generates recommendations for optimal pressure-reducing valve (PRV) setpoints — minimising pipe stress and reducing burst risk while maintaining adequate service pressure for all consumers.
Machine learning models profile each account’s normal consumption pattern and flag deviations that are statistically inconsistent with the historical baseline. This surfaces faulty meters that under-read, tampered meters, illegal connections diverting billed water, and properties with undetected internal leaks that will eventually generate customer complaints.
AI assigns a health score to critical assets — pumping stations, pressure sensors, control valves — based on their operating patterns over time. Degrading performance signatures are detected weeks before outright failure, enabling planned maintenance interventions that are far cheaper and less disruptive than emergency repairs.
The intelligence chain begins at the field device and ends with an actionable output in your operations system. ThingsLog low-power IoT loggers (connected via LoRa, NB-IoT, or 4G) collect readings at configurable intervals and transmit them to the ThingsLog cloud. On ingestion, data passes through a feature-engineering layer that normalises readings, fills missing values, and constructs the derived signals (flow balance, pressure gradient, night-flow index) on which ML models operate. Model inference runs continuously; outputs are ranked alerts, health scores, and forecasts that appear on the web dashboard, the mobile app, and — optionally — are pushed via API or Modbus TCP to your existing SCADA or GIS system for automated response.
District metered area (DMA) analysis with automated minimum night flow reporting, continuous pressure zone balancing, network-wide anomaly dashboards, and integration into billing and asset management systems. ThingsLog AI acts as a 24/7 operations analyst working across your entire network simultaneously.
Process water efficiency monitoring, effluent flow and quality compliance tracking, cooling tower optimisation to reduce blowdown waste, and detection of internal leaks in large industrial facilities where manual inspection of pipework is impractical.
AI-driven irrigation scheduling that fuses real-time soil moisture readings from ThingsLog field loggers with evapotranspiration models and short-range weather forecast data. Irrigation events are triggered only when the crop genuinely needs water — eliminating over-irrigation, reducing pumping energy, and improving yield quality.
A minimum of 3 to 6 months of baseline readings from pressure loggers and flow meters in each DMA zone is recommended before anomaly-detection models are put into live production. The more historical data available, the more accurate and seasonally aware the anomaly thresholds become. ThingsLog can begin delivering basic alerts from day one while the model continues to learn.
No. ThingsLog AI is designed to work alongside your existing SCADA or control infrastructure, not replace it. The platform enriches operational data with pattern analysis and early warnings, then passes actionable alerts to your control systems via REST API or Modbus TCP — so your operators continue working in familiar tools with significantly better information.
At the network anomaly-detection level — identifying that something is wrong in a specific DMA zone — detection accuracy of 85 to 95% is typical after model calibration on several months of site data. Individual leak localisation accuracy depends on sensor density within the zone and the hydraulic complexity of the network topology. ThingsLog can advise on optimal logger placement to maximise localisation precision.
Yes. ThingsLog models are retrained periodically on updated historical data, ensuring that anomaly thresholds, demand forecasts, and health scores continuously reflect current seasonal patterns, population changes, and any modifications made to the network infrastructure.
Contact ThingsLog to discuss AI-powered water monitoring for your utility and to schedule a demonstration using data from your own network.
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