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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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References

  1. Sattar AM, Gharabaghi B, McBean E (2016) Predicting timing of watermain failure using gene expression models for infrastructure planning. Water Resour Manag 30(5):1635–1651

    Article  Google Scholar 

  2. Schuster C, McBean E (2008) Impacts of cathodic protection on pipe break probabilities: a Toronto case study. Can J Civ Eng 35(2):210–216

    Article  Google Scholar 

  3. Nishiyama M, Filion Y (2013) Review of statistical water main break prediction models. Can J Civ Eng 40:972–979

    Article  Google Scholar 

  4. Kleiner Y, Sadiq R, Rajani B (2006) Modelling the deterioration of buried infrastructure as a fuzzy Markov process. J Water Supply Res Technol AQUA 55(2):67–80

    Article  Google Scholar 

  5. Huang G, Zhu Y, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  6. Atieh M, Mehltretter S, Gharabaghi B, Rudra R (2015a) Integrated neural networks model for prediction of sediment rating curve parameters for ungauged basins. J Hydrol 531(3):1095–1107. doi:10.1016/j.jhydrol.2015.11.008

    Article  Google Scholar 

  7. Atieh M, Gharabaghi B, Rudra R (2015b) Entropy-based neural networks model for flow duration curves at ungauged sites. J Hydrol 529(3):1007–1020. doi:10.1016/j.jhydrol.2015.08.068

    Article  Google Scholar 

  8. Atieh, M., Taylor, G., Sattar, A. M., & Gharabaghi, B. (2017). Prediction of flow duration curves for ungauged basins. Journal of Hydrology. Volume 545, February 2017, Pages 383–394, DOI: 10.1016/j.jhydrol.2016.12.048.

  9. Cao J, Xiong X (2014) Protein sequence classification with improved extreme learning machine algorithms. Biomed Res Int. doi:10.1155/2014/103054. Epub 2014 Mar 30

    Google Scholar 

  10. Ding S, Zhang J, Xu X, Yanan Z (2015) A wavelet extreme learning machine. Neural Comput & Applic 27(4):1033–1040

    Article  Google Scholar 

  11. Ding SF, Xu XZ, Nie R (2014) Extreme learning machine and its applications. Neural Comput. Appl 25(3):549–556

    Article  Google Scholar 

  12. Ertugrul O, Kaya M (2014) A detailed analysis on extreme learning machine and novel approaches based on ELM. American Journal of Computer Science and Engineering 1(5):43–50

    Google Scholar 

  13. Gazendam, E., Gharabaghi, B., Ackerman, J., & Whiteley, H. (2016). Integrative neural networks models for stream assessment in restoration projects. Journal of Hydrology, 536 (2016) 339-350. DOI: 10.1016/j.jhydrol.2016.02.057.

  14. Lian C, Zeng Z, Yao W, Tang H (2014) Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis. Neural Comput & Applic 24(1). doi:10.1007/s00521

  15. Luo M, Zhang K (2014) A hybrid approach combining extreme learning machine and sparse representation for image classification. Journal Engineering Applications of Artificial Intelligence archive 27:228–235

    Article  Google Scholar 

  16. Man Z, Huang G (2016) Guest editorial: special issue of extreme learning machine and applications. Neural Comput & Applic 27(1):1–2

    Article  Google Scholar 

  17. Sabouri F, Gharabaghi B, Sattar A, Thompson AM (2016) Event-based stormwater management pond runoff temperature model. Journal of Hydrology 540(2016):306–316. doi:10.1016/j.jhydrol.2016.06.017

    Article  Google Scholar 

  18. Trenouth WR, Gharabaghi B (2016) Highway runoff quality models for the protection of environmentally sensitive areas. Journal of Hydrology Volume 542(November 2016):143–155

    Article  Google Scholar 

  19. Cao J, Lin Z, Huang G, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77

    Article  MathSciNet  Google Scholar 

  20. Huang G, Zhou Y, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529

    Article  Google Scholar 

  21. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  22. Sattar AM (2014a) Gene expression models for the prediction of longitudinal dispersion coefficients in transitional and turbulent pipe flow. J. Pipeline Syst. Eng. Pract. ASCE 5(1):04013011

    Article  Google Scholar 

  23. Sattar AM (2016b) A probabilistic projection of the transient flow equations with random system parameters and internal boundary conditions. J Hydraul Res. doi:10.1080/00221686.2016.1140682

    Google Scholar 

  24. Sattar AM, Gharabaghi B (2015) Gene expression models for prediction of longitudinal dispersion coefficient in streams. J Hydrol 524:587–596

    Article  Google Scholar 

  25. Al-Barqawi M, Zayed T (2006) Condition rating model for underground infrastructure sustainable water mains. Journal of Performance of Constructed Facilities, ASCE. 20(2):126–135

    Article  Google Scholar 

  26. El Hakeem M, Sattar AM (2015) An entrainment model for non-uniform sediment. Earth Surf Process Landf. doi:10.1002/esp.3715

    Google Scholar 

  27. Najafzadeh M, Sattar AM (2015) Neuro-fuzzy GMDH approach to predict longitudinal dispersion in water networks. Water Resour Manag 29:2205–2219. doi:10.1007/s11269-015-0936-8

    Article  Google Scholar 

  28. Sattar AM, Dickerson J, Chaudhry M (2009) A wavelet Galerkin solution to the transient flow equations. J Hydraul Eng 135(4):283–295

    Article  Google Scholar 

  29. Sattar AM (2016a) Prediction of organic micropollutant removal in soil aquifer treatment system using GEP. J Hydrol Eng. doi:10.1061/(ASCE)HE.1943-5584.0001372 (in press)

    Google Scholar 

  30. Thompson J, Sattar A, Gharabaghi B, Warner R (2016) Event-based total suspended sediment particle size distribution model. J Hydrol 536(2016):236–246

    Article  Google Scholar 

  31. Vose D (1996) Quantitative risk analysis: a guide to Monte Carlo simulation modeling. John Wiley, New York

    MATH  Google Scholar 

  32. Walker H (1931) Studies in the history of the statistical method. Williams & Wilkins Co., Baltimore, MD, pp 24–25

    Google Scholar 

  33. Folkman S (2012) Water main break rates in the USA and Canada: a comprehensive study, report, Utah State University buried structures laboratory, April 2012.

  34. Rajani B, Kleiner Y, Sink JE (2012) Exploration of the relationship between water main breaks and temperature covariates. Urban Water 9(2):67–84

    Article  Google Scholar 

  35. Harvey R, McBean EA, Murphy HM, Gharabaghi B (2015) Using data mining to understand drinking water advisgories in small water systems: a case study of Ontario first nations drinking water supplies. Water Resources Management 29(14):5129–5139

    Article  Google Scholar 

  36. Harvey R, McBean E, Gharabaghi B (2014) Predicting the timing of water main failure using artificial neural networks. J Water Resour Plan Manag 140(4):425–434

    Article  Google Scholar 

  37. Harvey R, McBean EA, Gharabaghi B (2013) Predicting the timing of watermain failure using artificial neural networks. J Water Resour Plan Manag 140(4):425–434

    Article  Google Scholar 

  38. Verbeeck H, Samson R, Verdonck F, Raoul L (2006) Parameter sensitivity and uncertainty of the forest carbon flux model FOUG: a Monte Carlo analysis. Tree Physiol 26:807–817

    Article  Google Scholar 

  39. Goulter IC, Kazemi A (1998) Spatial and temporal groupings of water main pipe breakage in Winnipeg. Can J Civ Eng 15(1):91–97

    Article  Google Scholar 

  40. Asnaashari A, McBean EA, Gharabaghi B, Tutt D (2013) Forecasting watermain failure using artificial neural network modeling. Canadian Water Resources Journal 38(1):24–33

    Article  Google Scholar 

  41. Asnaashari A, McBean E, Gharabaghi B, Pourrajab R, Shahrour I (2010) Survival rate analyses of watermains: a comparison of case studies for Canada and Iran. Journal of Water Management Modeling 18(30):499–508

    Google Scholar 

  42. Asnaashari A, McBean EA, Shahrour I, Gharabaghi B (2009) Prediction of watermain failure frequencies using multiple and Poisson regression. Water Sci Technol Water Supply 9(1):9–19

    Article  Google Scholar 

  43. Rostum J (2000) Master of Science Dissertation. In: Statistical modeling of pipe failures in water networks. Norwegian University of Science and Technology, Trondheim, Norway

    Google Scholar 

  44. Lei J (1997) Statistical approach for describing lifetimes of water mains - case Trondheim Municpality. STF22 A97320, SINTEF, Trondheim.

  45. Wang Y, Moselhi O, Zayed T (2009) Study of the suitability of existing deterioration models for water mains. Journal of Performance of Constructed Facilities, ASCE 23(1):40–46

  46. Sattar AM (2014b) Gene expression models for prediction of dam breach parameters. Journal of Hydroinformatics, IWA 16(3):550–571

    Article  Google Scholar 

  47. Sattar AMA, El-Beltagy M (2017) Stochastic Solution to the Water Hammer Equations Using Polynomial Chaos Expansion with Random Boundary and Initial Conditions. J Hydraul Eng 143(2):04016078

  48. Ebtehaj I, Sattar AMA, Bonakdari H, Zaji AH (2017) Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. J Hydroinf 19(2):207–224

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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

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  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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