Monitoring water supply systems for anomaly detection and response

Water supply systems are vulnerable to damage caused by unintended or intended human actions, or due to aging of the system. In order to minimize the damages and the inconvenience for the customers, a software tool was developed to detect anomalies at an early stage, and to support the responsible staff in taking the right decisions to restore the normal situation. The software is designed for water quantity events as well as for water quality events. The model aims to detect events which occur relatively frequently in water distribution systems, like pipe bursts events and water discolouration events. The model does not aim to detect more severe and rare water contaminations.

[1]  Leonardo Alfonso,et al.  Multiobjective Optimization of Operational Responses for Contaminant Flushing in Water Distribution Networks , 2010 .

[2]  Joby Boxall,et al.  Online modelling of water distribution systems: a UK case study , 2009 .

[3]  Stefano Alvisi,et al.  A multi-objective approach for detecting and responding to accidental and intentional contamination events in water distribution systems , 2009 .

[4]  Louis Delorme,et al.  Heuristic Approach for Operational Response to Drinking Water Contamination , 2008 .

[5]  Man Bock Gu,et al.  An integrated mini biosensor system for continuous water toxicity monitoring. , 2005, Biosensors & bioelectronics.

[6]  Jan Vreeburg,et al.  The resuspension potential method: Yarra Valley water's novel approach to routine mains cleaning , 2007 .

[7]  Gunther F. Craun,et al.  Causes of Outbreaks Associated with Drinking Water in the United States from 1971 to 2006 , 2010, Clinical Microbiology Reviews.

[8]  E. A. Trietsch,et al.  Reliability of valves and section isolation , 2005 .

[9]  Avi Ostfeld,et al.  Multiobjective contaminant response modeling for water distribution systems security , 2008 .

[10]  Paola Verlicchi,et al.  Water Science and technology: Water Supply , 2013 .

[11]  Domenico Pianese,et al.  Stochastic approaches for sensors placement against intentional contaminations in water distribution systems , 2011 .

[12]  Gertjan Medema,et al.  Quantitative microbial risk assessment of distributed drinking water using faecal indicator incidence and concentrations. , 2007, Journal of water and health.

[13]  Stefano Alvisi,et al.  Near-optimal scheduling of device activation in water distribution systems to reduce the impact of a contamination event , 2012 .

[14]  Avi Ostfeld,et al.  Event detection in water distribution systems from multivariate water quality time series. , 2012, Environmental science & technology.

[15]  Jan Timmer,et al.  Flow control by prediction of water demand , 2003 .

[16]  Yosi Shacham-Diamand,et al.  Online monitoring of water toxicity by use of bioluminescent reporter bacterial biochips. , 2011, Environmental science & technology.

[17]  W. Hart,et al.  Review of Sensor Placement Strategies for Contamination Warning Systems in Drinking Water Distribution Systems , 2010 .

[18]  Dongxiao Zhang,et al.  A sparse grid based Bayesian method for contaminant source identification , 2012 .

[19]  Cynthia A. Phillips,et al.  Sensor Placement in Municipal Water Networks , 2003 .

[20]  S. Ranji Ranjithan,et al.  Monitoring Design for Source Identification in Water Distribution Systems , 2010 .

[21]  P.W.M.H. Smeets,et al.  The Dutch secret: how to provide safe drinking water without chlorine in the Netherlands , 2009 .

[22]  Andreas Krause,et al.  Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks , 2008 .

[23]  E. J. LeBoeuf,et al.  Consequence Management Optimization for Contaminant Detection and Isolation , 2006 .

[24]  A. Gerhardt,et al.  In situ on‐line toxicity biomonitoring in water: Recent developments , 2006, Environmental toxicology and chemistry.

[25]  Carl D. Laird,et al.  Real-time inversion in large-scale water networks using discrete measurements , 2012, Comput. Chem. Eng..

[26]  Lorenz T. Biegler,et al.  Mixed-Integer Approach for Obtaining Unique Solutions in Source Inversion of Water Networks , 2006 .

[27]  Joby Boxall,et al.  Field testing of an optimal sensor placement methodology for event detection in an urban water distribution network , 2010 .

[28]  Mark Bakker,et al.  Detecting pipe bursts by monitoring water demand , 2012 .

[29]  James G. Uber,et al.  Real-Time Identification of Possible Contamination Sources Using Network Backtracking Methods , 2010 .

[30]  Avi Ostfeld,et al.  A contamination source identification model for water distribution system security , 2007 .

[31]  Mustafa M. Aral,et al.  Identification of Contaminant Sources in Water Distribution Systems Using Simulation-Optimization Method: Case Study , 2006 .

[32]  J H G Vreeburg,et al.  Discolouration in potable water distribution systems: a review. , 2007, Water research.