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Platform

Hydro-ontology

A proprietary semantic framework that standardises geospatial entities and relationships so satellite, field, and model streams can integrate, reason, and scale. It is the core of Aurixys Twin — not a slide, a runtime graph.

Hydro-ontology coreSatellite, IoT, GIS, and field measurements feed a hydro-ontology of rivers, terrain, vessels, and weather. AI and physics models sit at the centre. Outputs are analytics, scenario simulations, and workflow alerts.Analytics & dashboardsScenario simulationsWorkflow alertsHYDRO-ONTOLOGYbuilt onsits onmonitorsfeedssamplesforcesRiverTerraincellsASV /ADCPWeatherstationsAI / PhysicsmodelsAIContinuous multi-source data streamSatellite · IoT · GIS · gaugesScience-informed AI and physics modelsHydrology · hydraulics · climate
Architecture of the Aurixys hydro-ontology: data and models in, operational products out.

What the graph holds

Scale is the point. A city twin and a basin twin are the same vocabulary at different resolution. Adding a new sensor or a new catchment is an entity, not a new product.

Entities

River, reach, terrain cell, city, drain, outfall, vessel, ADCP profile, gauge, storm. Each has a type, a geometry, and a history.

Relationships

Feeds, monitors, built-on, drains-to, forced-by. Models traverse the graph instead of joining ad-hoc files.

Observations

Satellite scenes, rainfall, chemistry, velocity profiles, and operator actions land as time-stamped facts on those entities.

Models

Hydrology, hydraulics, and learning systems read and write the same graph. Forecasts are first-class, with uncertainty attached.

Contact

Request a briefing.

For basin operators, city agencies, and partners who need a living model of water — not another static map.