Journal of Korean Society of Water and Wastewater (상하수도학회지)

The Korean Society of Water and Wastewater (대한상하수도학회)
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Probabilistic Neural Network for Prediction of Leakage in Water Distribution Network

급배수관망 누수예측을 위한 확률신경망

  • 하성룡 (충북대학교 도시공학과) ;
  • 류연희 (충북대학교 도시공학과 대학원) ;
  • 박상영 (한국수자원공사 수자원연구원)
  • Received : 2006.06.21
  • Accepted : 2006.12.06
  • Published : 2006.12.15
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Abstract

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.

Keywords

Acknowledgement

Supported by : 충북대학교

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