Study (Prediction) of Main Pipes Break Rates in Water Distribution Systems Using Intelligent and Regression Methods
Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is prepari...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Water and Wastewater Consulting Engineers Research Development
2011-07-01
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| Series: | آب و فاضلاب |
| Subjects: | |
| Online Access: | http://www.wwjournal.ir/article_1238_5d0c94aebe279768cad1519583c23779.pdf |
| Summary: | Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is preparing rehabilitation and replacement schemes, prediction of pipes break rate and evaluation of their reliability. Several approaches have been presented in recent years regarding prediction of pipe failure rates which each one requires especial data sets. Deterministic models based on age and deterministic multi variables and stochastic group modeling are examples of the solutions which relate pipe break rates to parameters like age, material and diameters. In this paper besides the mentioned parameters, more factors such as pipe depth and hydraulic pressures are considered as well. Then using multi variable regression method, intelligent approaches (Artificial neural network and neuro fuzzy models) and Evolutionary polynomial Regression method (EPR) pipe burst rate are predicted. To evaluate the results of different approaches, a case study is carried out in a part ofMashhadwater distribution network. The results show the capability and advantages of ANN and EPR methods to predict pipe break rates, in comparison with neuro fuzzy and multi-variable regression methods. |
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| ISSN: | 1024-5936 2383-0905 |