Comparative Study of Three Stochastic Models for Prediction of Pipe Failures in Water Supply Systems

Martins, André; Leitão, João P.; Amado, Conceição

doi:10.1061/(asce)is.1943-555x.0000154

Cited by 22 publications

(18 citation statements)

References 9 publications

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“…Lei and Saegrov, 1998;Le Gat and Eisenbeis, 2000;Pelletier et al, 2003;Alvisi and Franchini, 2010;Martins et al, 2013). The network sizes in the applications range from 155 to 1243 km.…”

Section: Model Applicationsmentioning

confidence: 99%

“…Martins et al, 2013;Claudio et al, 2014;. The length of the water distribution systems used to demonstrate the applicability of the models range from 367 to 3081 km.…”

Section: Model Applicationsmentioning

confidence: 99%

“…The covariates used in all three applications of the model are the logarithm of pipe length and the pipe diameter. Except in Martins et al (2013), additional pipe characteristics such as pipe location, bedding conditions, connection type, and climate data were used.…”

Section: Model Applicationsmentioning

confidence: 99%

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Statistical failure models for water distribution pipes – A review from a unified perspective

2015

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This review describes and compares statistical failure models for water distribution pipes in a systematic way and from a unified perspective. The way the comparison is structured provides the information needed by scientists and practitioners to choose a suitable failure model for their specific needs. The models are presented in a novel framework consisting of: 1) Clarification of model assumptions. The models originally formulated in different mathematical forms are all presented as failure rate. This enables to see differences and similarities across the models. Furthermore, we present a new conceptual failure rate that an optimal model would represent and to which the failure rate of each model can be compared. 2) Specification of the detailed data assumptions required for unbiased model calibration covering the structure and completeness of the data. 3) Presentation of the different types of probabilistic predictions available for each model. Nine different models and their variations or further developments are presented in this review. For every model an overview of its applications published in scientific journals and the available software implementations is provided. The unified view provides guidance to model selection. Furthermore, the model comparison presented herein enables to identify areas where further research is needed.

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“…Lei and Saegrov, 1998;Le Gat and Eisenbeis, 2000;Pelletier et al, 2003;Alvisi and Franchini, 2010;Martins et al, 2013). The network sizes in the applications range from 155 to 1243 km.…”

Section: Model Applicationsmentioning

confidence: 99%

“…Martins et al, 2013;Claudio et al, 2014;. The length of the water distribution systems used to demonstrate the applicability of the models range from 367 to 3081 km.…”

Section: Model Applicationsmentioning

confidence: 99%

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Statistical failure models for water distribution pipes – A review from a unified perspective

2015

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“…The main purpose of Water Distribution Networks (WDNs) is to supply water to the population in the required quantity and quality [1]. Factors such as climate change, deterioration of system components, uncertainty regarding the physical condition of the pipes, growing water demand, and economic restrictions increase the complexity of their management [2]. A proper strategy for operation, maintenance, and rehabilitation of WDNs needs to be developed to ensure efficient and reliable management.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Giraldo-González

Rodríguez

2020

Water

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The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

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“…Poisson models and NHPP (Non Homogeneous Poisson Process) have a good prediction of the (absolute value) failure rates compared to reality. LEYP (Linear Extended Yule Process) model is a model which synthesizes these two advantages (see figure 6) (Eisenbeis et al, 2002a;Eisenbeis et al, 2002b;Eisenbeis et al, 2003;Eisenbeis et al, 2004;Le Gat, 2009;Martins, 2011).…”

Section: Statistico-probabilistic Modelsmentioning

confidence: 99%

Decision Support Tools: Review of Risk Models in Drinking Water Network Asset Management

Large

Gat

Elachachi

et al. 2014

Vulnerability, Uncertainty, and Risk

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An objective of infrastructure asset management of drinking water network is to minimize risks (service outage, or disruption of vehicular traffic...). However there are several methods to assess their frequency. This paper first discusses the different models used in predicting the structural deterioration, because it is a compulsory preliminary step to calculate risk. Risk models for service outage are then discussed.

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Comparative Study of Three Stochastic Models for Prediction of Pipe Failures in Water Supply Systems

Cited by 22 publications

References 9 publications

Statistical failure models for water distribution pipes – A review from a unified perspective

Statistical failure models for water distribution pipes – A review from a unified perspective

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Decision Support Tools: Review of Risk Models in Drinking Water Network Asset Management

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