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Extreme learning machine model for water network management

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Abstract

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.

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Acknowledgments

The authors would like to thank the district of Scarborough for their contribution in the data collection phase and funding by the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs program.

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Authors and Affiliations

  1. Department of Irrigation & Hydraulics, Faculty of Engineering, Cairo University, Giza, Egypt

    Ahmed M. A. Sattar

  2. Department of Electrical and Electronics Engineering, Batman University, Batman, Turkey

    Ömer Faruk Ertuğrul

  3. School of Engineering, University of Guelph, NIG 2W1, Guelph, Ontario, Canada

    B. Gharabaghi & E. A. McBean

  4. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, 310018, China

    J. Cao

Authors
  1. Ahmed M. A. Sattar
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  2. Ömer Faruk Ertuğrul
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  3. B. Gharabaghi
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  4. E. A. McBean
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  5. J. Cao
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Corresponding author

Correspondence to B. Gharabaghi.

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Sattar, A.M.A., Ertuğrul, Ö., Gharabaghi, B. et al. Extreme learning machine model for water network management. Neural Comput & Applic 31, 157–169 (2019). https://doi.org/10.1007/s00521-017-2987-7

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  • DOI: https://doi.org/10.1007/s00521-017-2987-7

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