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
Optimise systems and modelling using artificial intelligence.
Machine learning (ML) is a type of artificial intelligence (AI) that helps software improve its predictive ability, automatically learning about data, patterns and outcomes without being explicitly programmed to do so.
ADASA harnesses these capabilities in a water resources context. We use machine learning to reduce asset management costs without compromising service levels. Predictive models can be generated from historical data on water deliveries, canal supply levels and assets as well as weather, electro-mechanical alarms and pay-rates and analysed to great use.
The future water consumption in a region or a water distribution network can be predicted, guaranteeing a continuous supply of water at a low cost. Machine learning can diagnose errors in a system and learn how to react and avoid future alarms. Thus includes dam behaviours and failures to efficient irrigation of crops.
Models created by using machine learning search for patterns within high volumes of data. Medical diagnostics, credit card fraud detection and face and speech recognition all use Machine Learning. Statistical algorithms extract features in the raw data. These features are then analysed via statistical analysis to produce a final data model for predictive use by the models.
We made use of machine learning for one of our clients to reduce asset management costs without compromising service levels. We collected data on water deliveries, canal supply levels, assets, weather, electro-mechanical alarms and pay-rates, and then built a model of the flow rate accuracy and supply level consistency. We have simulated the impact on service level making use of models that consider main variables related with asset management. Expensive maintenance activities such as desilting or removal of weeds in channels were examined for example to check if service improved as much as was generally believed.
Different algorithms are used to learn from data for different scenarios.
Typically, Machine learning processes are implemented in languages like Python (Keras), Scala, R, Java, C++.
Reducing energy and improving effluent in wastewater treatment.
Service Cost Modelling
Goulburn-Murray Water Corporation
Using machine learning to reduce asset management costs without compromising service levels.
Monitoring Water Quality at a Recirculating Aquaculture Facility
Seeᶟ Global Pty Ltd
Boosting the RAS productivity and increase efficiencies.