Comparing risk of failure models in water supply networks using ROC curves

Debón, Ana; Carrión, Andrés; Cabrera, Enrique; Solano, Hernando

doi:10.1016/j.ress.2009.07.004

Cited by 78 publications

(47 citation statements)

References 13 publications

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“…(1) was chosen for the comparative analysis to vividly demonstrate that many changeable parameters (e.g. pressure traffic [3]) should be taken into consideration in the prediction of the technical condition and the failure rate. Unfortunately, some detailed information, which theoretically should be included in the model, is difficult to obtain from water utilities in Poland and abroad.…”

Section: Methodsmentioning

confidence: 99%

“…Thanks to modelling one can relatively quickly assess the condition of water pipes [20]. According to many authors [3,15,17], the modelling of reliability indicators and technical condition and then using the modelling results by water utilities can lead to an increase in water supply network efficiency and in water quality. Also an improvement in the management of the system can be achieved in this way.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Failure Rate of Water-pipe Networks

Kutyłowska

2015

Period. Polytech. Civil Eng.

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The paper describes the reliability of selected water-pipe networks in Polish medium-sized cities X and Z. The IntroductionA water supply network is an essential part of the buried infrastructure. Water of proper quality should be delivered to consumers under the required pressure and in the required amount. In order to achieve these goals it is necessary to continuously monitor the technical condition of the water pipes. Proper maintenance and operation are critical for the reliability and safety of the water-pipe network. The control of the pressure inside the pipes is one of the measures leading to a decrease in the number of failures and in the unreliability of the whole system [4]. Another performance optimization measure is mathematical modelling and forecasting, which use typical models, artificial intelligence and some reliability indicators. Thanks to modelling one can relatively quickly assess the condition of water pipes [20]. According to many authors [3,15,17], the modelling of reliability indicators and technical condition and then using the modelling results by water utilities can lead to an increase in water supply network efficiency and in water quality. Also an improvement in the management of the system can be achieved in this way. Moreover, through modelling one can correctly estimate the costs of water pipe reconstruction [2,21].

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling of Failure Rate of Water-pipe Networks

Kutyłowska

2015

Period. Polytech. Civil Eng.

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“…Although the causes of pipe damage are complex and myriad, pressure transients play a role [11,2]. Internal pressure contributes to axial, longitudinal and hoop stress in pipes [26], and pressure transients may help cause both pipe bursts (associated with high-pressure events) and collapses (associated low-pressure events) [39].…”

Section: Reduction Of Variation In Pressurementioning

confidence: 99%

Optimizing Intermittent Water Supply in Urban Pipe Distribution Networks

Lieb¹,

Rycroft²,

Wilkening³

2016

SIAM J. Appl. Math.

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Abstract. In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets, and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. We demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure transients.1. Introduction. From the dry taps of Mumbai to the dusty reservoirs of São Paolo, urban water scarcity is a common condition of the present, and a likely feature of the future. Hundreds of millions of people worldwide are connected to water distribution systems subject to intermittency. This intermittent water supply may take many forms, from unexpected disruptions to planned supply cycles where pipes are filled and emptied regularly to shift water between different parts of the network at different times [17,33]. In Mumbai, for example, Vaivaramoorthy [33] reports that on average, residents have water flowing from their taps less than 8 out of 24 hours. Intermittent supply is often inequitable, with low-income neighborhoods experiencing lower water pressure and shorter supply durations than high-income ones [31]. Intermittent supply not only limits water availability, but also compromises water quality and damages infrastructure. With field data from urban India, Kumpel and Nelson [18] quantified the deleterious effect of intermittency on water quality, showing that both the initial flushing of water through empty pipes-as well as periods of low pressure-corresponded with periods of increased turbidity and bacterial contamination. Christodoulou [7] observed when that a drought in Cypress ushered in two years of intermittent supply, pipe ruptures increased by 30%-70% per year.Whereas intermittent water supply creates challenges for water managers and water users, the phenomenon creates opportunities for applied mathematics. It is an interesting and difficult mathematical problem to efficiently model transient pipe flow in networks-including transitions to and from pressurized states-with uncertain or complex boundary conditions. In this work we introduce a framework to not only describe intermittent water supply, but also use optimization to improve either our description of the system, or the operation of the described system in order to reduce risks such as infrastructure damage.Intermittent supply falls in somewhat of a modeling gap. Water distribution software abounds, including the free and open source software EPANET ...

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“…Secondly, the present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves following the methodology used for a pipe failure database by Debón et al (2010). A ROC curve is a technique for visualizing, organizing and selecting classifiers based on their performance.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

Debón

García-Díaz

2012

Reliability Engineering & System Safety

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Use of advanced statistical models can help industries to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are a tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures.The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to use them in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.

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Comparing risk of failure models in water supply networks using ROC curves

Cited by 78 publications

References 13 publications

Modelling of Failure Rate of Water-pipe Networks

Modelling of Failure Rate of Water-pipe Networks

Optimizing Intermittent Water Supply in Urban Pipe Distribution Networks

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

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