Optimal design of water networks using fuzzy genetic algorithm

Amirabdollahian, Mahsa; Chamani, Mohammad R.; Asghari, Keyvan

doi:10.1680/wama.2011.164.7.335

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The proposed approach was found to handle uncertainties effectively. Amirabdollahian et al (2011) used a fuzzy GA (FGA) with the objective of least-cost design for two WDN and found it to provide better optimum solutions. Shibu & Reddy (2014) employed cross-entropy optimization under demand uncertainty represented as a triangular fuzzy number and applied to two WDN.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy optimization-based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm

2021

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Water Distribution Network(s) (WDN) design is gaining prominence in the urban planning context. Several factors that play a significant role in design are uncertainty in data, non-linear relation of head loss & discharge, combinatorial nature of the problem, and high computational requirements. In addition, many conflicting objectives are possible and required for effective WDN design, such as cost, resilience, and leakage. Most of the research work published has used multiobjective evolutionary optimization in solving such complex WDN. However, the challenge of such population based evolutionary approaches is that they provide multiple trade-off Pareto optimal solutions to the decision-maker who will have to choose another set of techniques to arrive at a single optimal solution. The present study employs a fuzzy optimization approach that would provide a single optimal WDN design for Hanoi and Pamapur, India. Maximization of network resilience (NR) and minimization of network cost (NC) are employed in a multiobjective context. Later, minimization of network leakages (NL) is also incorporated, leading to three objective problems. Hyperbolic Membership Function (HMF), Exponential Membership Function (EMF), and Non-linear Membership Function (NMF) are employed in Self-Adaptive Cuckoo Search Algorithm based fuzzy optimization. HMF is found suitable to determine the best possible WDN design for chosen case studies based on the highest degree of satisfaction. HIGHLIGHT Most of the research conducted till now have used evolutionary multiobjective optimization in solving WDNs. But, the challenge of such evolutionary approaches is that they provide multiple trade-off pareto optimal solutions to the decision maker who will have to further choose another methodology to converge to a single optimal solution. The proposed methodology would simplify the decision-making process for an engineer.

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Section: Introductionmentioning

confidence: 99%

Fuzzy optimization-based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm

2021

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“…In the fuzzy design approach, uncertain input variables are entered into the optimization process as a fuzzy set and then the WDN is designed with the least cost of the WDN's equipment construction such as pipes, pumps, valves, tanks, etc. and the highest performance during the operation period (Bozorg-Haddad et al 2017;Donger and Gupta 2017;Shibu and Reddy 2012;Farmani et al 2005aFarmani et al , 2005bDridi et al 2009;Amirabdollahian et al 2011;Mosavian and Lence 2018). In the fuzzy uncertainty analysis, the extraction of output parameters membership functions such as nodes demand and pressure and pipes flow velocity and their analysis is considered (Sabzkouhi and Haghighi 2016;Branisavljevic and Ivetic 2006;Revelli and Ridolfi 2002;Spiliotis and Tsakiris 2012;Shibu and Reddy 2011;Gupta and Bhave 2007;Moosavian and Lence 2018;Geranmehr et al 2019;Gupta et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of WDN Pipes Repair and Replacement Optimal Instruction Using EPANET Simulation Model and Fuzzy Cut Approach Combination

Jafari

Zahiri

Hadad

et al. 2021

Preprint

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Optimization of the initial design or pipes repair and replacement instructions of water distribution network during operation period is based on the crisp values of the input variables. Some input variables such as node demand, pipe roughness coefficient and reservoir water level have the uncertain nature. Changing the input parameters values during operation period, due to uncertainty, changes the water distribution network behavior and performance compared to the crisp input parameters values. Recognition and analyzing these behaviors are very important to make the right decision to deal with their consequences and reduce the water distribution network problems, during operation period. In this research, the water distribution network nodes pressure uncertainty due to the pipe's roughness coefficient and the nodes demand uncertainty as input parameters, is analyzed after the implementation of the optimal pipes repair and replacement instruction. For the purpose, a combination of simulation model (EPANET) and fuzzy α-cut approach is used. Fuzzy membership functions of the input and output variables are selected as triangular type and the membership functions values, after normalization, were in the range of zero and one. The extreme values combinations of two uncertain input variables, at each uncertainty level, in the form of four scenarios, were considered as the input fuzzy set of the simulation model. Between the research scenarios, the second scenario, which is the combination of the minimum pipe roughness coefficient and the maximum demand, is known as the critical scenario. The research results show that in the critical scenario, at the highest uncertainty level, the water distribution network reliability index is very low and about 30 to 40% due to lack of required pressure of the most nodes. At the uncertainty lower levels, the reliability index rises above 75%, which is relatively acceptable. In the first and fourth scenarios, the network reliability index is always more than 72%. In the third scenario, the network reliability index, at all uncertainty levels, is always more than 90%.

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“…In the paper by Amirabdollahian et al (2011) a fuzzy genetic algorithm is used to obtain the least-cost design of looped water distribution networks. In this approach fitness evaluation is carried out using a fuzzy decision system.…”

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confidence: 99%

Editorial

Thornton¹

2011

Proceedings of the Institution of Civil Engineers - Water Manag

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The papers in this issue of Water Management offer an insight into new developments related to the management of water supply and quality in urban and rural catchments. They cover the design of rainfall monitoring networks, water distribution systems and storm run-off barriers. New modelling approaches are introduced that integrate the uncertainty within data sets and complex interrelationships between system components in decisionmaking. Improvements in the design performance and operation of infrastructure for irrigation systems are illustrated, and the application of a novel low-technology concept for urban stormwater management is described.The first paper describes a methodology that uses entropy theory coupled with sequential algorithms (SA) and genetic algorithms (GA) to determine the location of new rainfall stations for collection of rainfall data (Karimi-Hosseini et al., 2011). This has a variety of relevant applications in water projects. The study considers the desire to optimise the distribution of the network stations and increase the quality of data obtained by them through growth of the network. The value of entropy theory is that it is an efficient method of uncertainty analysis in water resource systems and can be used as a measure of information transfer and available information in monitoring networks. The methodology is evaluated with a case study in Iran.Transinformation entropy-distance relationships are generated to capture the spatial variability of transinformation entropy across the study area, as an aid to identifying the optimum locations for new rainfall stations. The algorithms are used in two ways. The SA uses a step-by-step selection procedure which does not consider the effect of other new stations on the transinformation entropy of the network. In contrast, the GA considers the effect of all stations on the transinformation entropy of the network simultaneously. The performance of four models is compared in the analysis, in which the SA and GA are employed to maximise transinformation entropy in defining new network locations for rain gauges. A key observation is that the performance of the two algorithms in determining the priority of rain gauge site installation was different and this difference increased with an increase in the number of new stations added. Overall, the study concludes that the GA is superior in developing the rain gauge network for the design criteria used. These include economic, efficiency and precision-based parameters, which can be extended to consider other goals in the management of a watershed. The implications with respect to using rainfall data in relevant management decisions and predictions (e.g. estimating precipitation, flood forecasting, integrated water resource management, river basin water balances or future network development) are discussed.Optimising the design of water distribution networks in urban areas, industrial plants or irrigation systems requires that both performance and cost-criteria be considered. Statistical methods and mode...

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Optimal design of water networks using fuzzy genetic algorithm

Cited by 6 publications

References 24 publications

Fuzzy optimization-based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm

Fuzzy optimization-based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm

Uncertainty Analysis of WDN Pipes Repair and Replacement Optimal Instruction Using EPANET Simulation Model and Fuzzy Cut Approach Combination

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