| Records |
| Author |
Thomas Bernard; Mathias Braun; Olivier Piller; Denis Gilbert; Jochen Deuerlein; Andreas Korth; Reik Nitsche; Marie Maurel; Anne-Claire Sandraz; Fereshte Sedehizade; Jean-Marc Weber; Caty Werey |
| Title |
SMaRT-OnlineWDN: Online security management and reliability toolkit for water distribution networks |
Type |
Conference Article |
| Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
| Volume |
|
Issue |
|
Pages |
171-176 |
| Keywords |
Computer simulation; Contamination; Decision support systems; Industrial management; Information systems; Runoff; Water quality; Water supply; Abnormal conditions; Online simulation; Operation and control; Sensor measurements; Source identification; Transport modeling; Water distribution networks; Water supply networks; Water distribution systems |
| Abstract |
Water distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental contamination. Until now, no monitoring system is capable of protecting a WDN in real time. In the immediate future water service utilities that are installing water quantity and quality sensors in their networks will be producing a continuous and huge data stream for treating. The main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for water distribution networks that is based on sensor measurements of water quality as well as water quantity and online simulation. Its field of application ranges from detection of deliberate contamination, including source identification and decision support for effective countermeasures, to improved operation and control of a WDN under normal and abnormal conditions. |
| Address |
Fraunhofer Institute IOSB, Germany; IRSTEA, France; 3S Consult GmbH, Germany; DVGW-Technologiezentrum Wasser, Germany; Veolia Environnement, France; Veolia Eau d'Ile de France, France; Berliner Wasserbetriebe, Germany; Communauté Urbaine de Strasbourg, Germany; Engees, France |
| Corporate Author |
|
Thesis |
|
| Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
|
| Track |
Critical Infrastructures |
Expedition |
|
Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
no |
| Call Number |
|
Serial |
313 |
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| Author |
Han Che; Shuming Liu |
| Title |
Monitoring data identification for a water distribution system based on data self-recognition approach |
Type |
Conference Article |
| Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
| Volume |
|
Issue |
|
Pages |
166-170 |
| Keywords |
Information systems; Random processes; Statistics; Water distribution systems; ARMA model; Autoregressive moving average model; Contamination events; Data identification; Outlier identification; Prediction confidence; Self-recognition; Water distribution networks; Monitoring |
| Abstract |
Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology. |
| Address |
Tsinghua University, China |
| Corporate Author |
|
Thesis |
|
| Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
|
| Track |
Critical Infrastructures |
Expedition |
|
Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
no |
| Call Number |
|
Serial |
387 |
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