GIS, network, NRW

WaterSense

A GIS-based platform for real-time monitoring of production, distribution networks, non-revenue water and water quality.

AI-based anomaly detectionOpen architectureReal-time monitoring

Operational outcomes

  • Central visibility across plant, network and customer metrics
  • AI-powered anomaly detection
  • DMA and NRW analytics

Core capabilities

  • GIS network map
  • Flow and pressure analytics
  • AI anomaly detection
  • Water quality monitoring

Integrations

  • SCADA
  • IoT logger
  • GIS
  • Billing
  • Data warehouse

Product overview

A GIS operations layer for the whole water network

WaterSense brings network maps, DMAs, SCADA/IoT signals, water-quality readings and customer data into one operating screen so teams can detect anomalies, localize losses and coordinate action faster.

WaterSense

Core functional layers

01

Operational GIS

Manage pipes, valves, meters, DMAs and network assets on a contextual map.

02

DMA & NRW

Track flow, pressure, minimum night flow and loss indicators by zone.

03

AI alerts

Identify abnormal patterns from real-time data and prioritize areas that need action.

04

Water quality

Monitor online readings and alert when configured operating thresholds are exceeded.

Process

Recommended rollout flow

  1. 1

    Standardize the network map

    Review GIS data, asset IDs and DMA boundaries before operations go live.

  2. 2

    Connect source systems

    Integrate SCADA, loggers, GIS, billing and existing data stores.

  3. 3

    Configure operations logic

    Set thresholds, alert rules, dashboards and anomaly-detection models.

  4. 4

    Coordinate field response

    Route alerts into inspection, confirmation and result-reporting workflows.

Deployment model

Deployed on existing data and infrastructure

Best for utilities with separate GIS, SCADA, logger or billing systems that need one operating layer for the distribution network.

Best fit when

  • The network must be viewed by DMA and asset
  • Leak and loss detection needs to be faster
  • Many data sources are not yet unified

Deployment items

  • Baseline data-quality review
  • Secure API or database integration
  • Operator training by shift workflow

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