Steve Mounce
Steve Mounce
Visiting Research Fellow, University of Sheffield
Verified email at - Homepage
Cited by
Cited by
Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows
SR Mounce, JB Boxall, J Machell
Journal of Water Resources Planning and Management 136 (3), 309-318, 2010
Novelty detection for time series data analysis in water distribution systems using support vector machines
SR Mounce, RB Mounce, JB Boxall
Journal of hydroinformatics 13 (4), 672-686, 2011
Burst detection using hydraulic data from water distribution systems with artificial neural networks
SR Mounce, J Machell
Urban Water Journal 3 (1), 21-31, 2006
Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system
SR Mounce, A Khan, AS Wood, AJ Day, PD Widdop, J Machell
Information Fusion 4 (3), 217-229, 2003
Online modelling of water distribution systems: a UK case study
J Machell, SR Mounce, JB Boxall
Drinking Water Engineering and Science 3 (1), 21-27, 2010
Water loss reduction
ZY Wu, M Farley, D Turtle, Z Kapelan, J Boxall, S Mounce, ...
Bentley Institute Press, 2011
A neural network approach to burst detection
SR Mounce, AJ Day, AS Wood, A Khan, PD Widdop, J Machell
Water science and technology 45 (4-5), 237-246, 2002
Field testing of an optimal sensor placement methodology for event detection in an urban water distribution network
B Farley, SR Mounce, JB Boxall
Urban Water Journal 7 (6), 345-356, 2010
Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data
SR Mounce, W Shepherd, G Sailor, J Shucksmith, AJ Saul
Water science and technology 69 (6), 1326-1333, 2014
Development and field validation of a burst localization methodology
B Farley, SR Mounce, JB Boxall
Journal of Water Resources Planning and Management 139 (6), 604-613, 2013
Pattern matching and associative artificial neural networks for water distribution system time series data analysis
SR Mounce, RB Mounce, T Jackson, J Austin, JB Boxall
Journal of Hydroinformatics 16 (3), 617-632, 2014
An artificial intelligence approach for optimizing pumping in sewer systems
S Ostojin, SR Mounce, JB Boxall
Journal of hydroinformatics 13 (3), 295-306, 2011
Addressing practical problems in sustainability assessment frameworks
L Hurley, R Ashley, S Mounce
Proceedings of the Institution of Civil Engineers-Engineering Sustainability …, 2008
Modelling both the continual erosion and regeneration of discolouration material in drinking water distribution systems
WR Furnass, RP Collins, PS Husband, RL Sharpe, SR Mounce, JB Boxall
Water Science and Technology: Water Supply 14 (1), 81-90, 2014
Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems
SR Mounce, K Ellis, JM Edwards, VL Speight, N Jakomis, JB Boxall
Water Resources Management 31 (5), 1575-1589, 2017
Relating water quality and age in drinking water distribution systems using self-organising maps
EJ Blokker, WR Furnass, J Machell, SR Mounce, PG Schaap, JB Boxall
Environments 3 (2), 10, 2016
Implementation of an on-line artificial intelligence district meter area flow meter data analysis system for abnormality detection: a case study
SR Mounce, JB Boxall
Water Science and Technology: Water Supply 10 (3), 437-444, 2010
Optimal locations of pressure meters for burst detection
B Farley, JB Boxall, SR Mounce
Water Distribution Systems Analysis 2008, 1-11, 2008
An artificial neural network/fuzzy logic system for DMA flow meter data analysis providing burst identification and size estimation
SR Mounce, JB Boxall, J Machell
Water management challenges in global change, 313-320, 2007
Cloud based machine learning approaches for leakage assessment and management in smart water networks
SR Mounce, C Pedraza, T Jackson, P Linford, JB Boxall
Procedia engineering 119, 43-52, 2015
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