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Canadian Water Resources Journal / Revue canadienne des ressources hydriques Volume 38, 2013 - Issue 1: Tackling Challenging Water Resources Problems in Canada: A Systems Approach
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Articles

Forecasting watermain failure using artificial neural network modelling

Ahmad Asnaashari Water Eng. Faculty, Power and Water University of Technology (PWUT), Tehran, Iran; School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1Correspondence[email protected]
,
Edward A. McBean School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1
,
Bahram Gharabaghi School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1
&
Donald Tutt School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1
Pages 24-33 | Published online: 28 Mar 2013

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Sean Kerwin, Borja Garcia de Soto, Bryan Adey, Kleio Sampatakaki & Hannes Heller. (2023) Combining recorded failures and expert opinion in the development of ANN pipe failure prediction models. Sustainable and Resilient Infrastructure 8:1, pages 86-108.
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Dnyaneshwar Vasant Wadkar, Rahul Subhash Karale & Manoj Pandurang Wagh. (2022) Application of cascade feed forward neural network to predict coagulant dose. Journal of Applied Water Engineering and Research 10:2, pages 87-100.
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Brett Snider & Edward A. McBean. (2020) Watermain breaks and data: the intricate relationship between data availability and accuracy of predictions. Urban Water Journal 17:2, pages 163-176.
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Bryson Robertson, Bahram Gharabaghi & Kevin Hall. (2015) Prediction of Incipient Breaking Wave-Heights Using Artificial Neural Networks and Empirical Relationships. Coastal Engineering Journal 57:4, pages 1550018-1-1550018-27.
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André Marques Arsénio, Prabu Dheenathayalan, Ramon Hanssen, Jan Vreeburg & Luuk Rietveld. (2015) Pipe failure predictions in drinking water systems using satellite observations. Structure and Infrastructure Engineering 11:8, pages 1102-1111.
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Hadi Memarian, Siva Kumar Balasundram & Mohamad Tajbakhsh. (2013) An expert integrative approach for sediment load simulation in a tropical watershed. Journal of Integrative Environmental Sciences 10:3-4, pages 161-178.
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Keith W. Hipel, Liping Fang, Taha B.M.J. Ouarda & Michele Bristow. (2013) An Introduction to the special issue on tackling challenging water resources problems in Canada: a systems approach. Canadian Water Resources Journal / Revue canadienne des ressources hydriques 38:1, pages 3-11.
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Ibrahim Abdelfadeel Shaban, Abdelrahman E.E. Eltoukhy & Tarek Zayed. (2023) Systematic and scientometric analyses of predictors for modelling water pipes deterioration. Automation in Construction 149, pages 104710.
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Atefeh Delnaz, Fuzhan Nasiri & S. Samuel Li. (2023) Asset management analytics for urban water mains: a literature review. Environmental Systems Research 12:1.
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Ridwan Taiwo, Ibrahim Abdelfadeel Shaban & Tarek Zayed. (2023) Development of sustainable water infrastructure: A proper understanding of water pipe failure. Journal of Cleaner Production 398, pages 136653.
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Burak Kizilöz. (2022) Prediction of daily failure rate using the serial triple diagram model and artificial neural network. Water Supply 22:9, pages 7040-7058.
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N. A. Barton, S. H. Hallett, S. R. Jude & T. H. Tran. (2022) An evolution of statistical pipe failure models for drinking water networks: a targeted review. Water Supply 22:4, pages 3784-3813.
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Seyed Mehran Jafari, Omid Bozorg-Haddad & Mohammad Reza Nikoo. 2022. Computational Intelligence for Water and Environmental Sciences. Computational Intelligence for Water and Environmental Sciences 333 354 .
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Brett Snider & Edward A. McBean. (2021) Combining Machine Learning and Survival Statistics to Predict Remaining Service Life of Watermains. Journal of Infrastructure Systems 27:3.
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Dnyaneshwar Vasant Wadkar, Prakash Nangare & Manoj Pandurang Wagh. (2021) Evaluation of water treatment plant using Artificial Neural Network (ANN) case study of Pimpri Chinchwad Municipal Corporation (PCMC). Sustainable Water Resources Management 7:4.
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Thikra Dawood, Emad Elwakil, Hector Mayol Novoa & José Fernando Gárate Delgado. (2021) Toward urban sustainability and clean potable water: Prediction of water quality via artificial neural networks. Journal of Cleaner Production 291, pages 125266.
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Farzad KarimianKhalid Kaddoura, Tarek ZayedAlaa HawariOsama Moselhi. (2021) Prediction of Breaks in Municipal Drinking Water Linear Assets. Journal of Pipeline Systems Engineering and Practice 12:1.
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Thikra Dawood, Emad Elwakil, Hector Mayol Novoa & José Fernando Gárate Delgado. (2020) Artificial intelligence for the modeling of water pipes deterioration mechanisms. Automation in Construction 120, pages 103398.
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Nehal Elshaboury & Mohamed Marzouk. (2020) Comparing Machine Learning Models For Predicting Water Pipelines Condition. Comparing Machine Learning Models For Predicting Water Pipelines Condition.
Brett Snider & Edward A. McBean. (2020) Improving Urban Water Security through Pipe-Break Prediction Models: Machine Learning or Survival Analysis. Journal of Environmental Engineering 146:3.
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Neal Andrew Barton, Timothy Stephen Farewell, Stephen Henry Hallett & Timothy Francis Acland. (2019) Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks. Water Research 164, pages 114926.
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Ahmed M. A. Sattar, Ömer Faruk Ertuğrul, B. Gharabaghi, E. A. McBean & J. Cao. (2017) Extreme learning machine model for water network management. Neural Computing and Applications 31:1, pages 157-169.
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Yvonne L. PostEdward McBeanBahram Gharabaghi. (2018) Using Probabilistic Neural Networks to Analyze First Nations’ Drinking Water Advisory Data. Journal of Water Resources Planning and Management 144:11.
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Fang Shi, Yihao Liu, Zheng Liu & Eric Li. (2018) Prediction of pipe performance with stacking ensemble learning based approaches. Journal of Intelligent & Fuzzy Systems 34:6, pages 3845-3855.
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Fragkoulis Papagiannis, Patrizia Gazzola, Olena Burak & Ilya Pokutsa. (2018) Overhauls in water supply systems in Ukraine: A hydro-economic model of socially responsible planning and cost management. Journal of Cleaner Production 183, pages 358-369.
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David A. Lloyd Owen. 2018. Assessing Global Water Megatrends. Assessing Global Water Megatrends 87 104 .
Fang Shi, Zheng Liu & Eric Li. (2017) Prediction of Pipe Performance with Ensemble Machine Learning Based Approaches. Prediction of Pipe Performance with Ensemble Machine Learning Based Approaches.
Harsh Vardhan SinghAnita M. ThompsonBahram Gharabaghi. (2017) Event Runoff and Sediment-Yield Neural Network Models for Assessment and Design of Management Practices for Small Agricultural Watersheds. Journal of Hydrologic Engineering 22:2.
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Ibrahim BakryHani AlzraieeKhalid KaddouraMohamed El MasryTarek Zayed. (2016) Condition Prediction for Chemical Grouting Rehabilitation of Sewer Networks. Journal of Performance of Constructed Facilities 30:6.
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Ibrahim BakryHani AlzraieeMohamed El MasryKhalid KaddouraTarek Zayed. (2016) Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains. Journal of Performance of Constructed Facilities 30:5.
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Ahmed M. A. Sattar, B. Gharabaghi & Edward A. McBean. (2016) Prediction of Timing of Watermain Failure Using Gene Expression Models. Water Resources Management 30:5, pages 1635-1651.
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William R. Trenouth & Bahram Gharabaghi. (2015) Event-based soil loss models for construction sites. Journal of Hydrology 524, pages 780-788.
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Richard HarveyEdward A. McBeanBahram Gharabaghi. (2014) Predicting the Timing of Water Main Failure Using Artificial Neural Networks. Journal of Water Resources Planning and Management 140:4, pages 425-434.
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