Abstract
This paper presents an application of Artificial Neural Networks (ANN) to model the failure rate and estimate the optimal replacement time for the individual pipes in an urban water distribution system. The performances of the ANN are examined using a 14-year data set collected in a city in the north of France. The first part of the paper presents the collected data. The second part describes the construction and validation of six ANN models. After a discussion of the performances of these models, they are used for the prediction of water mains failure and the determination of the benefit index, which allows optimization of investment for the rehabilitation and maintenance of urban water mains. The spatial repartition of the risk of degradation is illustrated using a geographic information system, which constitutes an effective tool for the elaboration of strategies of rehabilitation of water distribution systems.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1170-1180 |
| Number of pages | 11 |
| Journal | Mathematical and Computer Modelling |
| Volume | 51 |
| Issue number | 9-10 |
| DOIs | |
| State | Published - May 2010 |
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Keywords
- ANN
- Artificial Neural networks
- Failure
- GIS
- Mains
- Predictions
- Statistics
- Urban water
ASJC Scopus subject areas
- Computer Science Applications
- Modeling and Simulation
Cite this
Application of Artificial Neural Networks (ANN) to model the failure of urban water mains. / Jafar, Raed; Shahrour, Isam; Juran, Ilan.
In: Mathematical and Computer Modelling, Vol. 51, No. 9-10, 05.2010, p. 1170-1180.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Application of Artificial Neural Networks (ANN) to model the failure of urban water mains
AU - Jafar, Raed
AU - Shahrour, Isam
AU - Juran, Ilan
PY - 2010/5
Y1 - 2010/5
N2 - This paper presents an application of Artificial Neural Networks (ANN) to model the failure rate and estimate the optimal replacement time for the individual pipes in an urban water distribution system. The performances of the ANN are examined using a 14-year data set collected in a city in the north of France. The first part of the paper presents the collected data. The second part describes the construction and validation of six ANN models. After a discussion of the performances of these models, they are used for the prediction of water mains failure and the determination of the benefit index, which allows optimization of investment for the rehabilitation and maintenance of urban water mains. The spatial repartition of the risk of degradation is illustrated using a geographic information system, which constitutes an effective tool for the elaboration of strategies of rehabilitation of water distribution systems.
AB - This paper presents an application of Artificial Neural Networks (ANN) to model the failure rate and estimate the optimal replacement time for the individual pipes in an urban water distribution system. The performances of the ANN are examined using a 14-year data set collected in a city in the north of France. The first part of the paper presents the collected data. The second part describes the construction and validation of six ANN models. After a discussion of the performances of these models, they are used for the prediction of water mains failure and the determination of the benefit index, which allows optimization of investment for the rehabilitation and maintenance of urban water mains. The spatial repartition of the risk of degradation is illustrated using a geographic information system, which constitutes an effective tool for the elaboration of strategies of rehabilitation of water distribution systems.
KW - ANN
KW - Artificial Neural networks
KW - Failure
KW - GIS
KW - Mains
KW - Predictions
KW - Statistics
KW - Urban water
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UR - http://www.scopus.com/inward/citedby.url?scp=77649188485&partnerID=8YFLogxK
U2 - 10.1016/j.mcm.2009.12.033
DO - 10.1016/j.mcm.2009.12.033
M3 - Article
AN - SCOPUS:77649188485
VL - 51
SP - 1170
EP - 1180
JO - Mathematical and Computer Modelling
JF - Mathematical and Computer Modelling
SN - 0895-7177
IS - 9-10
ER -