Preprints
https://doi.org/10.5194/dwes-2021-7
https://doi.org/10.5194/dwes-2021-7

  29 Mar 2021

29 Mar 2021

Review status: this discussion paper is a preprint. It has been under review for the journal Drinking Water Engineering and Science (DWES). The manuscript was not accepted for further review after discussion.

Implementing and evaluating various machine learning models for pipe burst prediction

Ahmad Ravanbakhsh1, Mehdi Momeni2, and Amir Robati2 Ahmad Ravanbakhsh et al.
  • 1PhD in Water Resources Engineering and Management, Department of Civil Engineering, Azad University of Kerman, Iran
  • 2Assistant Professor, Department of Civil Engineering, Azad University of Kerman, Iran

Abstract. By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve the level of services in water distribution networks (WDNs). In this study, we aimed to implement five artificial intelligence and machine learning regression models such as multivariate adaptive regression splines (MARS), M5' regression tree (M5'), Least square support vector regression (LS-SVR), fuzzy regression based on c-means clustering (FCMR) and regressive convolution neural network with support vector regression (RCNN-SVR) for predicting pipe burst rate and evaluating the performance of these models. The most effective parameters for regression models are pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure. In the present study, collected data include 158 cases for polyethylene (PE) and 124 cases for asbestos cement (AC) pipes during 2012-2019. The results indicate that the RCNN-SVR model has a great performance of pipe burst rate (PBR) prediction.

Ahmad Ravanbakhsh et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on dwes-2021-7', Anonymous Referee #1, 26 Apr 2021
    • AC1: 'Reply on RC1', ahmad ravanbakhsh, 02 May 2021
  • CC1: 'Comment on dwes-2021-7', Rana Muhammad Adnan Ikram, 02 May 2021
    • AC3: 'Reply on CC1', ahmad ravanbakhsh, 11 May 2021
  • RC2: 'Comment on dwes-2021-7', Martijn Bakker, 04 May 2021
    • AC2: 'Reply on RC2', ahmad ravanbakhsh, 08 May 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on dwes-2021-7', Anonymous Referee #1, 26 Apr 2021
    • AC1: 'Reply on RC1', ahmad ravanbakhsh, 02 May 2021
  • CC1: 'Comment on dwes-2021-7', Rana Muhammad Adnan Ikram, 02 May 2021
    • AC3: 'Reply on CC1', ahmad ravanbakhsh, 11 May 2021
  • RC2: 'Comment on dwes-2021-7', Martijn Bakker, 04 May 2021
    • AC2: 'Reply on RC2', ahmad ravanbakhsh, 08 May 2021

Ahmad Ravanbakhsh et al.

Data sets

joopar Ac and PE pipes Ravanbakhsh, Ahmad https://doi.org/10.5281/zenodo.4587385

Model code and software

Regression matlab codes Ravanbakhsh, Ahmad https://doi.org/10.5281/zenodo.4587392

Ahmad Ravanbakhsh et al.

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Short summary
Pipe burst in water distribution networks is an inevitable event. Pipe burst prediction helps to manage the maintenance of pipes, which reduce costs, water consumption and increase water network reliability. In this paper, we implement, compare and evaluate five artificial intelligence and machine learning methods for pipe failure prediction. Pipe failure data were collected during an eight-year-period in a real case study. Finally, the best method is selected based on some error criteria.