Extreme learning machine model for water network management
Yükleniyor...
Tarih
2017-04-22
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Nature
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.
Açıklama
Anahtar Kelimeler
Extreme Machine Learning, Management Tool, Pipe Failure, Water Pipe Network
Kaynak
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
31
Sayı
1
Künye
Sattar, A. M., Ertuğrul, Ö F., Gharabaghi, B., Mcbean, E. A., & Cao, J. (2017). Extreme learning machine model for water network management. Neural Computing and Applications, 31(1), pp. 157-169. https://doi.org/10.1007/s00521-017-2987-7
