HOWS is a three-year EPSRC-funded project run by the University of Exeter's Centre for Water Systems, which will investigate interactive methods for designing and managing water systems, incorporating visual analytics, heuristic optimisation and machine learning methods.
The overall aim of the HOWS project is to develop a new approach for designing and managing improved, near-optimal and engineering-intuitive water systems by incorporating visual analytics, heuristic optimisation and feedback-informed learning.
It is widely acknowledged that the water and wastewater infrastructure assets, which communities rely upon for health, economy and environmental sustainability, are severely underfunded on a global scale. For example, a funding gap of nearly $55 billion has been identified by the US EPA (ASCE, 2011). In England and Wales, the total estimated capital value of water utility assets is £254.8 billion (Ofwat, 2015), but between 2010 and 2015 only £12.9 billion was allocated for maintaining and replacing assets. Combined with the drive to reduce customers' bills, there will be even more pressure on water companies to find ways to bridge the gap between the available and required finances. As a result of this it is not surprising that optimisation methods have been extensively researched and applied in this area (Maier et al., 2014).
The inability of those methods to include into optimisation 'unquantifiable' or difficult to quantify, yet important considerations, such as user subjective domain knowledge, has contributed to the limited adoption of optimisation in the water industry. Many cognitive and computational challenges accompany the design, planning and management involving complex engineered systems. Water industry infrastructure assets (i.e., water distribution and wastewater networks) are examples of systems that pose severe difficulties to completely automated optimisation methods due to their size, conceptual and computational complexity, non-linear behaviour and often discrete/combinatorial nature. These difficulties have first been articulated by Goulter (1992), who primarily attributed the lack of application of optimisation in water distribution network (WDN) design to the absence of suitable professional software. Although such software is now widely available (e.g., InfoWorks, WaterGems, EPANET, etc.), the lack of user under-standing of capabilities, assumptions and limitations still restricts the use of optimisation by practicing engineers (Walski, 2001).
Automatic methods that require a purely quantitative mathematical representation do not leverage human expertise and can only find solutions that are optimal with regard to an invariably over-simplified problem formulation. The focus of the past research in this area has almost exclusively been on algorithmic issues. However, this approach neglects many important human-computer interaction issues that must be addressed to provide practitioners with engineering-intuitive, practical solutions to optimisation problems. 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.
The new approach will be extensively tested on problems provided by the UK water industry and will involve practicing engineers and experts in this important problem domain.
To develop a flexible application for the intuitive visualisation, simulation and optimisation of WDNs.
To develop the application to enable continual learning wherein practicing engineers come to under-stand the optimisation process through interaction with the software system.
To work in collaboration with key industry representatives from the technology sector (Virtalis, XP Solutions), water (Bristol Water, South West Water) and consulting (SEAMS Ltd).
To create new visual analytics capability for water distribution network design.
Learn optimisation heuristics from human interaction and embed them within the approach.
Develop real-world performance metrics that characterise 'engineering intuitive' solutions.
To evaluate these methods on benchmark problems (e.g. Anytown, Exenet - Wang et al., 2015); to establish a relationship between 'mathematical optimality' and 'engineering intuitive' solutions with the aim to create more 'engineering intuitive' objective functions.
To use the system to engage water industry experts and to investigate the stages of the learning process, test user intervention strategies and the performance improvements thereof.
To experiment with the parameters of the intervention strategies to reduce fatigue and maximise useful human input to the optimisation process.
To use the developed system to optimise the design, rehabilitation and operation of real-world water systems derived from the industrial users, e.g., the Exeter network with over 21,000 pipes (see the letter of support from South West Water).
We are working with the following industrial and academic partners
Professor of Hydroinformatics
Professor Savic (FREng, FICE, FCIWEM, FIWA, MASCE, CEng) is the UK's first Professor of Hydroinformatics having held this post at the University of Exeter since 2001. His research interests cover the interdisciplinary field of Hydroinformatics.
Associate Professor in Computer Science
Prof. Ed Keedwell is an Associate Professor in Computer Science. He joined the Computer Science discipline in 2006 having previously been a Research Fellow in the Centre for Water Systems and was appointed as a lecturer in Computer Science in 2009.
Herman Mahmoud is a Research Fellow in the Centre for Water Systems, College of Engineering and Physical Sciences, University of Exeter. Herman graduated at the University of Dohuk in 2010 with MSc degree in Water Resources Engineering, and obtained his PhD in Engineering at the University of Exeter.
Matthew Johns is a Research Fellow in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. After obtaining degrees in Mechanical Engineering (BEng) and Engineering and Management (MSc) he embarked on a PhD in Computer Science at the University of Exeter.
Impact and Partnership Development Manager
Nicky works within the Environment, Food Security and Sustainability theme within Innovation, Impact and Business. Her work supports impact development, research and innovation collaborations involving external partners (particularly within the water industry), broader partnership development and income generation across the University.
Nick Ross is a PhD Student in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, undertaking research in the Gamification of Optimisation of Water Distribution Systems.
For a complete archive of HOWS news stories click here.
Researchers from the HOWS project showcased the Augmented Evolutionary Intelligence system yesterday at an event at Exeter University discussing the latest research and industry innovation in Digital Twins. The system combines virtual reality, machine learning and evolutionary computation to leverage the power of human intuition and AI working together to solve engineering problems.
We are pleased to announced that our paper entitled Human-Evolutionary Problem Solving through Gamification of a Bin-Packing Problem has been accepted to the iGECCO Workshop at the Genetic and Evolutionary Computation Conference (GECCO 2019) in Prague, Czech Republic in July 2019. The paper describes recent work on human-AI interaction through a game interface to optimise the well known operations research problem of bin-packing.
We are pleased to announced that our paper entitled Augmented Evolutionary Intelligence: Combining Human and Evolutionary Design for Water Distribution Network Optimisation has been accepted to the Genetic and Evolutionary Computation Conference (GECCO 2019) in Prague, Czech Republic in July 2019. The paper describes recent work on the HOWS project combining user interaction, machine learning and evolutionary algorithms to improve the way that water network optimisation is conducted.
As a first step in the HOWS project, the team are gathering industry opinions about the use of optimisation within the water industry. If you are a water industry professional engaged in optimisation work, we would be delighted to hear your thoughts. Please take a few minutes to fill out the survey, which can be found here.