Dr Steve Mounce

BSc, MSc, PhD

Department of Civil and Structural Engineering

Visiting Research Fellow

s.r.mounce@sheffield.ac.uk

Full contact details

Dr Steve Mounce
Department of Civil and Structural Engineering
Sir Frederick Mappin Building (Broad Lane Building)
Mappin Street
Sheffield
S1 3JD
Profile

After promotion to Research Fellow in 2011, Dr. Mounce was an investigator on research projects with a total value of £7.5 M, including £1.9 M to the University of Sheffield (£470 k PI and £1.43 M CoI).

Since 2015 he worked part-time whilst developing his own micro- company (Mounce HydroSmart) and now holds a Visiting Research Fellow position.

Steve has a Degree in Mathematics with Specials Honours from Hull University in Mathematics, a Masters Degree in Computer Science at the University of Bradford in 1994 and was awarded a doctorate in Computer Science at the University of Bradford in 2005 (A Hybrid Neural Network Rule-Based System applied to Leak Detection in Water Pipeline Distribution Networks). He joined the Pennine Water Group in 2005.

During this period he worked on and led a number of research projects including:

  • YWS funded ADA project - Researching an automated computing system for the detection and location of leakage in water distribution systems using Artificial Neural Networks (ANNs) and Fuzzy Logic
  • EPSRC NEPTUNE project - Delivering sustainable water systems by optimising existing infrastructure via improved knowledge, understanding and technology
  • EPSRC Pipe Dreams project - New knowledge, tools and techniques to maximise the performance of existing ageing buried pipe infrastructure
  • EU FP7 SmartWater4Europe project - Developing and demonstrating for four European test sites an integrated solution for smart management of water distribution including leakage control, water quality management and energy optimisation (PI)
  • EU H2020 CENTAUR project to provide an innovative, cost effective, local autonomous sewer flow control system to reduce urban flood risk (investigator)
Qualifications
  • PhD Computer Science / Engineering, University of Bradford
  • MSc Computing, University of Bradford
  • BSc Special Honours Mathematics, University of Hull.
Research interests

His main research areas are:

  • Application of Artificial Intelligence and machine learning techniques such as Artificial Neural Networks (Design, implementation and training using various architectures including MLP, Kohonen SOM, Time Delay, recurrent, associative and Mixture Density Network), Fuzzy Logic and Knowledge Based Systems for classification, prediction and pattern recognition applications. Experience with other data driven methodologies such as Support Vector Machines, ensemble decision trees and Genetic Algorithms. Latest research interests involve Deep learning, ontologies and knowledge extraction for intelligent agents/ chatbots and blockchain as a service.
  • Active in applying computer science skills to water engineering and hydroinformatics research in the areas of leakage (including smart meters), CSO analytics, water quality and burst event detection systems, fuzzy RTC, data mining, case based reasoning, sustainability assessment and knowledge management.
Professional activities
  • Invited peer reviewer for high quality international journals, including Water Research, HydroInformatics, American Society of Civil Engineers Water Resources Planning and Management, ICE Water Management, IME Engineering for the Maritime Environment, Water Resources Management, Environmental Software and Modelling, Advances in Water Resources and Environmental Research. Reviewer for the Royal Society, National Science Centers and international PhD external examiner.
  • Winner of the 2010 IWEX University Challenge (Presented at Sustainabilitylive! 2010).
  • Commercialisation of Artificial Intelligence based automated analysis research software (FlowSure) by Servelec, £100,000 IP assignment deal. (Datective Flowsure). Nominated for ‘Innovative Technology’ Water Industry Achievement Award 2017 with Welsh Water for application to wastewater network.