Robust Design of Water Distribution Networks for a Proactive Risk Management

Cunha, Maria da Conceição; Sousa, Joaquim

doi:10.1061/(asce)wr.1943-5452.0000029

Cited by 47 publications

(20 citation statements)

References 32 publications

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“…Afonso and Cunha (2007) developed a robust model to design biological reactors and secondary settling tanks in wastewater treatment plants. Cunha and Sousa (2010) presented models for the robust design of water distribution networks to enable them to face the uncertainty of network working conditions under extreme events. Zeferino et al (2012) have recently proposed a robust optimization model for the sitting and sizing of wastewater treatment plants at regional level that includes uncertainty issues associated with river flows.…”

Section: A Chebbi Et Al: Development Of a Methods Of Robust Rain Gaumentioning

confidence: 99%

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Chebbi

Bargaoui

Cunha

2013

Hydrol. Earth Syst. Sci.

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Abstract. Based on rainfall intensity-duration-frequency (IDF) curves, fitted in several locations of a given area, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimization can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables, and their spatial interdependence is taken into account through the adoption of crossvariograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short-and a longterm horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km 2 ). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. Therefore, it is proposed to augment it by 25, 50, 100 and 160 % virtually, which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

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Section: A Chebbi Et Al: Development Of a Methods Of Robust Rain Gaumentioning

confidence: 99%

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Chebbi

Bargaoui

Cunha

2013

Hydrol. Earth Syst. Sci.

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“…nodes 7 and 12, for fire flows 1 and 2, respectively (Table 1). Consequently, the minimum nodal head was higher for the peak demand than the fire flows (Walski et al 1987;Farmani et al 2006;Cunha and Sousa 2010). The properties of the pipes are shown in Table 2.…”

Section: Details Of the Network Investigatedmentioning

confidence: 99%

“…In reality, however, the demands vary with the time of the day and other loading conditions including fire flows that have to be satisfied by the network (Cunha and Sousa 2010;Simpson et al 1994;Walski et al 1987;Tanyimboh and Seyoum 2016). Alperovits and Shamir (1977) suggested that, in addition to the maximum daily demand and fire flows, the minimum demand periods should be considered also.…”

Section: Introductionmentioning

confidence: 99%

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Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Tanyimboh

2017

Water Resour Manage

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Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.

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“…Simulated annealing is a randomized search method [59] that is known to quickly find good solutions, even in extended search spaces. This method has been used to solve different problems from the water supply field, as documented in the literature: least cost design of WDN [60], robust design of WDN [61,62], optimal operation of WDN [63], optimal design and operation of WDN [55,64], pressure management in WDN [65], District Metered Areas design [66], and optimization of reservoir operation [67,68].…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Locating Leaks with TrustRank Algorithm Support

Ribeiro

Sousa

Marques

et al. 2015

Water

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This paper presents a methodology to quantify and to locate leaks. The original contribution is the use of a tool based on the TrustRank algorithm for the selection of nodes for pressure monitoring. The results from these methodologies presented here are: (I) A sensitivity analysis of the number of pressure transducers on the quality of the final solution; (II) A reduction of the number of pipes to be inspected; and (III) A focus on the problematic pipes which allows a better office planning of the inspection works to perform atthe field. To obtain these results, a methodology for the identification of probable leaky pipes and an estimate of their leakage flows is also presented. The potential of the methodology is illustrated with several case studies, considering different levels of water losses and different sets of pressure monitoring nodes. The results are discussed and the solutions obtained show the benefits of the developed methodologies.

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Robust Design of Water Distribution Networks for a Proactive Risk Management

Cited by 47 publications

References 32 publications

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Locating Leaks with TrustRank Algorithm Support

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