### Abstract

Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical model. If significant variables are known, without knowing the exact relationships, ANN is suitable to perform a kind of function fitting by using multiple parameters on the existing information and predict the possible relationships in the near future. This is the case in the water distribution network design or operation problems wherein the input (pipe diameters, lengths, age, soil, etc...)-output (reliability of the network) relationship is given by the set of nonlinear continuity equations, path head loss equations and the head-discharge relationship. This paper introduces a methodology of establishing ANN of modeling the pipe breaks from which rehabilitation strategies (proactive maintenance strategy), prioritization of rehabilitation implementation, finding the optimum time for rehabilitation of the pipe and determining the parameters that most affect the likelihood of pipe breaks, can be determined for predicting the number of breaks for each individual pipe in the water distribution system of Benghazi city (WDSB). Because this work is a part of a research has not completed yet, this paper presents only the modeling technique using ANN to achieve the main objective which is; expected number of pipe breaks.

Original language | English |
---|---|

Title of host publication | Proceedings - 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011 |

Pages | 50-57 |

Number of pages | 8 |

DOIs | |

Publication status | Published - 2011 |

Event | 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011 - Penang Duration: 4 Mar 2011 → 6 Mar 2011 |

### Other

Other | 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011 |
---|---|

City | Penang |

Period | 4/3/11 → 6/3/11 |

### Fingerprint

### Keywords

- Artificial Neural Network
- pipe break
- prediction
- rehabilitation strategy
- Water distribution system

### ASJC Scopus subject areas

- Signal Processing

### Cite this

*Proceedings - 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011*(pp. 50-57). [5759841] https://doi.org/10.1109/CSPA.2011.5759841

**Application of artificial neural networks in modeling water networks.** / Bubtiena, Abdelwahab M.; Elshafie, Ahmed H.; Jafaar, Othman.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings - 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011.*, 5759841, pp. 50-57, 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011, Penang, 4/3/11. https://doi.org/10.1109/CSPA.2011.5759841

}

TY - GEN

T1 - Application of artificial neural networks in modeling water networks

AU - Bubtiena, Abdelwahab M.

AU - Elshafie, Ahmed H.

AU - Jafaar, Othman

PY - 2011

Y1 - 2011

N2 - Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical model. If significant variables are known, without knowing the exact relationships, ANN is suitable to perform a kind of function fitting by using multiple parameters on the existing information and predict the possible relationships in the near future. This is the case in the water distribution network design or operation problems wherein the input (pipe diameters, lengths, age, soil, etc...)-output (reliability of the network) relationship is given by the set of nonlinear continuity equations, path head loss equations and the head-discharge relationship. This paper introduces a methodology of establishing ANN of modeling the pipe breaks from which rehabilitation strategies (proactive maintenance strategy), prioritization of rehabilitation implementation, finding the optimum time for rehabilitation of the pipe and determining the parameters that most affect the likelihood of pipe breaks, can be determined for predicting the number of breaks for each individual pipe in the water distribution system of Benghazi city (WDSB). Because this work is a part of a research has not completed yet, this paper presents only the modeling technique using ANN to achieve the main objective which is; expected number of pipe breaks.

AB - Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical model. If significant variables are known, without knowing the exact relationships, ANN is suitable to perform a kind of function fitting by using multiple parameters on the existing information and predict the possible relationships in the near future. This is the case in the water distribution network design or operation problems wherein the input (pipe diameters, lengths, age, soil, etc...)-output (reliability of the network) relationship is given by the set of nonlinear continuity equations, path head loss equations and the head-discharge relationship. This paper introduces a methodology of establishing ANN of modeling the pipe breaks from which rehabilitation strategies (proactive maintenance strategy), prioritization of rehabilitation implementation, finding the optimum time for rehabilitation of the pipe and determining the parameters that most affect the likelihood of pipe breaks, can be determined for predicting the number of breaks for each individual pipe in the water distribution system of Benghazi city (WDSB). Because this work is a part of a research has not completed yet, this paper presents only the modeling technique using ANN to achieve the main objective which is; expected number of pipe breaks.

KW - Artificial Neural Network

KW - pipe break

KW - prediction

KW - rehabilitation strategy

KW - Water distribution system

UR - http://www.scopus.com/inward/record.url?scp=79957487214&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957487214&partnerID=8YFLogxK

U2 - 10.1109/CSPA.2011.5759841

DO - 10.1109/CSPA.2011.5759841

M3 - Conference contribution

AN - SCOPUS:79957487214

SN - 9781612844145

SP - 50

EP - 57

BT - Proceedings - 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011

ER -