AquaLearning: Machine Learning for Water Management
Platform to predict the behaviour of aquatic systems at real time developed applying machine learning algorithms.
The innovation is that AquaLearning allows for quick detection of several features such as minority and trace pollutants, groundwater levels, etc., using only available rutinary information.
- Economic savings (analysis reduction)
- Quick response
- Management optimization
- Risk reduction
Sustainable Aquifer Management
Quantitative decision making tool for the optimization of wells exploitation. Historical data is used to characterize the aquifer response and the wells performance. The resulting algorithms allows to identify the optimal extraction flows in order to:
- minimize drawdowns in wells
- avoid overexploitation
- control pollutant concentrations
- reduce energy costs
Optimization of Water Supply Services
To date, the management of the integral water cycle has generated a high volume of data including demands, served volumes, water quality, treatment efficiency, energy needs, …. The analysis of this data with machine learning tools allows optimizing management in terms of:
- Analytical optimization (prediction of the presence of certain pollutants in supply and waste water)
- Energy saving
- Process improvement
- Consumption patterns and hygiene habits
- Risk assessment in the reuse of reclaimed water
Identification and Early Warning of Water Pollution
Water contamination can be related to punctual and diffuse origins of a different nature. Remediation and prevention actions needs to know these origins and the causes in order to develop the most adequate management strategy.
Artificial intelligence and machine learning are applied to these problems to identify the causes of pollution, its origin and to predict concentrations and impacts.
It becomes a basic tool to set prevention and remediation measures.