The Centre for Water Systems has undertaken the task of linking AQUATOR to a Multiobjective Genetic Algorithms optimisation module

Artificial Intelligence research and applications

We are actively researching in many fields including techniques taken from the field of artificial intelligence such as genetic algorithms, cellular automata, neural networks, genetic programming.

We are continually looking for new ways to apply these techniques to more and more complex problems in the water industry including:

  • Water Distribution System/Operation Optimisation
  • Sewer System Optimisation
  • Hydrology Applications
  • Water Re-use 
  • RecyclingWater Quality Optimisation
  • Decision Support Tools for the Water Industry


Human-Computer Optimisation for Water Systems Planning and Management (HOWS)

This project will develop new understanding of how engineering design, planning and management of complex water systems can be improved by creating a visual analytics optimisation approach that will integrate human expertise (through 'human in the loop' interactive optimisation), IT infrastructure (cloud/parallel computing) and state-of-the-art optimisation techniques to develop highly optimal, engineering intuitive solutions for the water industry.

iWIDGET Project: Smart water; smart meters; smart societies (2012 - 2015)

Improved water efficiency through ICT technologies for integrated supply-demand side management iWIDGET is a European Commission project aimed at improved water efficiencies through the use of novel ICT technologies for integrated supply-demand side management. It is a project funded under the EU 7th framework Programme, which started in November 2012 and will run for 3 years.

GA-Aquator: integrated optimisation for reservoir operation using genetic algorithms (2006-present)

AQUATOR® is a commercial software for developing and running simulation models of natural rivers, water resources and water supply systems, using different operational rules, constraints and priorities

GANetXL (2006 - ongoing)

GANetXL is a general-purpose decision-support system generator for developing specific applications that require multiobjective optimisation of spreadsheet-based models.

Development Of Genetic Programming Techniques For Water Industry Applications (1998-2001)

The objective identified in the proposal was the investigation of potential applications for genetic programming (GP) that would be of benefit to the water industry.

Data mining techniques for risk assessment (1998-2000)

The project deals with the use of data mining techniques on the Royal Mail risk database. A sample database was supplied on which encouraging results were found. The data mining techniques employed here each attempt to find patterns and trends in a database with greater accuracy than standard statistical techniques.

Rainfall-runoff modelling using neural networks and genetic programming (1997)

This project presents an application of Neural Networks (NNs) to rainfall-runoff modelling. Applications of the neural network technique in this domain of hydrology have so far provided accurate results for small storm events on theoretical catchments (Minns & Hall, 1995).

Symbolic regression using object-oriented genetic programming (1996)

Data driven modelling techniques have gained in popularity in the last 20 years. They are more cost effective compared to the development of mechanistic models. Furthermore, those mechanistic models are highly non-linear and complex, which makes them difficult to identify and use.