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Development of multi-objective optimization model for water distribution network using a new reliability index

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Abstract

The high cost of pipe repair and replacement and the importance of the hydraulic performance in water distribution networks have faced the management of these networks with serious management challenges. The solution to these challenges is providing an optimal instruction of pipes repair and replacement with minimum cost and maximum hydraulic performance, which is the principal purpose of this research. A new hydraulic performance index was developed, considering the demand deficit of nodes. The proposed approach of this research is a combination of a hydraulic simulation model, a hybrid prediction model of pipes failure rate, a multi-objective optimization model, and a voting multi-criteria decision-making model. The approach is used in part of Gorgan's water distribution network. Implementing the optimal instruction of pipe repair and replacement improves the quality of pipes (70% reduction in pipe failure rate) and increases the nodes' average pressure and network reliability index by 94.3% and 52.64%, respectively.

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References

  • Abdy Sayyed MAH, Gupta R, Tanyimboh TT (2015) Noniterative application of EPANET for pressure dependent modelling of water distribution systems. Water Resour Manag 29(9):3227–3242

    Article  Google Scholar 

  • Altunkaynak A, Nigussie TA (2017) Monthly water consumption prediction using season algorithm and wavelet transform–based models. J Water Resour Plan Manag 143(6):04017011. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000761

    Article  Google Scholar 

  • Anele A, Todini E, Hamam Y, Abu-Mahfouz A (2018) Predictive uncertainty estimation in water demand forecasting using the model conditional processor. Water 10:475

    Article  Google Scholar 

  • Asnaashari A, McBean EA, Gharabaghi B, Tutt D (2013) Forecasting watermain failure using artificial neural network modelling. Can Water Resour J 38(1):24–33

    Article  Google Scholar 

  • Baños R, Reca J, Martínez J, Gil C, Márquez AL (2011) Resilience indexes for water distribution network design: a performance analysis under demand uncertainty. Water Resour Manag 25(10):2351–2366

    Article  Google Scholar 

  • Bello AD, Waheed A, Alayande JA, Ismail A, Lawan UF (2015) Optimization of the designed water distribution system using MATLAB. Int J Hydraul Eng 4(2):37–44

    Google Scholar 

  • Belton V, Stewart TJ (2002) Multiple criteria decision analysis. Springer, Boston

    Book  Google Scholar 

  • De Borda JC (1781) Memoire sur les elections au scrutin. Histoire de l’Academie Royale desSciences, Paris

  • Bozorg-Haddad O, Ghajarnia N, Solgi M, Loáiciga HA, Mariño MA (2017) Multi-objective design of water distribution systems based on the fuzzy reliability index. J Water Supply Res Technol AQUA 66(1):36–48. https://doi.org/10.2166/aqua.2016.067

    Article  Google Scholar 

  • Brams SJ, Fishburn PC (1978) Approval voting. Am Pol Sci Rev 72(3):831–847

    Article  Google Scholar 

  • Chen G, Long T, Xiong J, Bai Y (2017) Multiple random forests modelling for urban water consumption forecasting. Water Resour Manag 31:4715–4729

    Article  Google Scholar 

  • Cheung PB, Reis LFR, Formiga KTM, Chaudhry FH, Ticona WGC (2003) Multiobjective evolutionary algorithms applied to the rehabilitation of a water distribution system: a comparative study. In: Fonseca Carlos M, Fleming Peter J, Zitzler Eckart, Thiele Lothar, Deb Kalyanmoy (eds) Evolutionary multi-criterion optimization. Springer, Berlin, pp 662–676. https://doi.org/10.1007/3-540-36970-8_47

    Chapter  Google Scholar 

  • Colebrook CF, White CM (1937) The reduction of carrying capacity of pipes with age. J Inst Civil Eng 7(1):99–118

    Article  Google Scholar 

  • Condorcet M (1785) Essai sur l’application de l’analyse a la probabilite des de’cisions rendues a la pluralite des voix. Imprimerie Royale, Paris

  • Dandy GC, Engelhardt M (2001) Optimal scheduling of water pipe replacement using genetic algorithms. J Water Resour Plan Manag 127(4):214–223

    Article  Google Scholar 

  • de Almeida AT, Morais C, Nurmi H (2019) Choosing a voting procedure for a water resources management problem. In: de Almeida AT, Morais DC, Nurmi H (eds) Systems, procedures and voting rules in context: a primer for voting rule selection. Springer International Publishing, Cham, pp 163–175. https://doi.org/10.1007/978-3-030-30955-8_16

    Chapter  Google Scholar 

  • de Almeida-Filho AT, Monte MBS, Morais DC (2017) A voting approach applied to preventive maintenance management of a water supply system. Group Decis Negot 26:523–546

    Article  Google Scholar 

  • De Souza GG, Costa MA, Libânio M (2019) Predicting water demand: a review of the methods employed and future possibilities. Water Supply 12:1628

    Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Dini M, Tabesh M (2018) A new reliability index for evaluating the performance of water distribution network. J Water Wastew 29(3):1–16 (In Persian)

    Google Scholar 

  • Donkor EA, Mazzuchi TH, Soyer R, Roberson JA (2014) Urban water demand forecasting: review of methods and models. J Water Resour Plan Manag 140:146–159

    Article  Google Scholar 

  • Dridi L, Parizeau M, Mailhot A, Villeneuve JP (2008) Using evolutionary optimization techniques for scheduling water pipe renewal considering a short planning horizon. Comput Aided Civil Infrastruct Eng 23(8):625–635

    Article  Google Scholar 

  • Dridi L, Mailhot A, Parizeau M, Villeneuve JP (2009) Multi-objective approach for pipe replacement based on Bayesian inference of break model parameters. J Water Resour Plan Manag 135(5):344–354

    Article  Google Scholar 

  • Elshaboury N, Attia T, Marzouk M (2020) Application of evolutionary optimization algorithms for rehabilitation of water distribution networks. J Constr Eng Manag 146(7):04020069

    Article  Google Scholar 

  • Farmani R, Kakoudakis K, Behzadian Moghadam K, Butler D (2017) Pipe failure prediction in water distribution systems considering static and dynamic factors. Procedia Eng 186:117–126

    Article  Google Scholar 

  • Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241

    Article  Google Scholar 

  • Fontana ME, Morais DC (2017) Water distribution network segmentation based on group multi-criteria decision approach. Production. https://doi.org/10.1590/0103-6513.208316

    Article  Google Scholar 

  • Fu G, Butler D, Khu ST, Sun SA (2011) Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory. Water Resour Res. https://doi.org/10.1029/2009WR008944

    Article  Google Scholar 

  • Ghalehkhondabi I, Ardjmand E, Young WA, Weckman GR (2017) Water demand forecasting: review of soft computing methods. Environ Monit Assess 189:313

    Article  Google Scholar 

  • Gheisi A, Naser G (2016) Multi-aspect performance analysis of water distribution systems under pipe failure. Procedia Eng 119:158–167

    Article  Google Scholar 

  • Goetz RU, Martinez Y, Rodrigo J (2007) Water allocation by social choice rules: the case of sequential rules. Ecol Econ 65:304–314

    Article  Google Scholar 

  • Gorev NB, Kodzhespirova IF (2013) Noniterative implementation of pressure-dependent demands using the hydraulic analysis engine of EPANET 2. Water Resour Manag 27(10):3623–3630

    Article  Google Scholar 

  • Guo G, Liu S, Yipeng W, Li J, Zhou R, Zhu X (2018) Short-term water demand forecast based on deep learning method. J Water Resour Plan Manag 144(12):04018076. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000992

    Article  Google Scholar 

  • Harvey R, McBean EA, Gharabaghi B (2013) Predicting the timing of water main failure using artificial neural networks. J Water Resour Plan Manag 140(4):425–434

    Article  Google Scholar 

  • Hashim H, Ryan P, Clifford E (2020) A statistically based fault detection and diagnosis approach for non-residential building water distribution systems. Adv Eng Inf 46:101187

    Article  Google Scholar 

  • Jafar R, Shahrour I, Juran I (2010) Application of artificial neural networks (ANN) to model the failure of urban water mains. Math Comput Model 51(9–10):1170–1180

    Article  Google Scholar 

  • Jafari SM, Zahiri AR, Bozorg-Haddad O, Tabari MMR (2020) New approach for prediction of water distribution network pipes failure based on an intelligent hybrid model (case study: gorgan water distribution network). J Water Soil Conserv. https://doi.org/10.22069/JWSC.2021.17670.3319(inPersian)

    Article  Google Scholar 

  • Jafari SM, Zahiri AR, Bozorg-Haddad O, Tabari MMR (2021) A hybrid of six soft models based on ANFIS for pipe failure rate forecasting and uncertainty analysis: a case study of Gorgan city water distribution network. Soft Comput. https://doi.org/10.1007/s00500-021-05706-4

    Article  Google Scholar 

  • Kabir G, Sadiq R, Tesfamariam SA (2013) Review of multi-criteria decision-making methods for infrastructure management. Struct Infrastruct Eng 10:1176–1210

    Article  Google Scholar 

  • Kapelan ZS, Savic DA, Walters GA (2003) A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks. J Hydraul Res 41(5):481–492

    Article  Google Scholar 

  • Kerwin S, de Soto, BG, Adey BT (2019) Performance comparison for pipe failure prediction using artificial neural networks. In: 6th international Symposium on life-cycle civil engineering, IALCCE, pp1337–1342. CRC Press/Balkema

  • Kim K, Seo J, Hyung J, Kim T, Kim J, Koo J (2019) Economic-based approach for predicting optimal water pipe renewal period based on risk and failure rate. Environ Eng Res 24(1):63–73

    Article  Google Scholar 

  • Liu H, Savić DA, Kapelan Z, Creaco E, Yuan Y (2017) Reliability surrogate measures for water distribution system design: Comparative analysis. J Water Resour Plan Manag 143(2):04016072

    Article  Google Scholar 

  • Madani K, Read L, Shalikarian L (2014) Voting under uncertainty: a stochastic framework for analyzing group decision making problems. Water Resour Manag 28(7):1839–1856

    Article  Google Scholar 

  • Meshram SG, Ghorbani MA, Deo RC, Kashani MH, Meshram C, Karimi V (2019) New approach for sediment yield forecasting with a two-phase feedforward neuron network-particle swarm optimization model integrated with the gravitational search algorithm. Water Resour Manag 33:2335–2356

    Article  Google Scholar 

  • Monsef H, Naghashzadegan M, Jamali A, Farmani R (2019) Comparison of evolutionary multi objective optimization algorithms in optimum design of water distribution network. Ain Shams Eng J 10(1):103–111

    Article  Google Scholar 

  • Morais DC, Almeida AT (2010) Water network rehabilitation: a group decision-making approach. Water SA 36(4):487–493

    Article  Google Scholar 

  • Peña-Guzmán C, Melgarejo J, Prats D (2016) Forecasting water demand in residential, commercial, and industrial zones in bogotá, colombia, using least-squares support vector machines. Math Probl Eng 2016:1–10

    Article  Google Scholar 

  • Salehi S, Jalili Ghazizadeh M, Tabesh M (2018) A comprehensive criteria-based multi-attribute decision-making model for rehabilitation of water distribution systems. Struct Infrastruct Eng 14(6):743–765

    Article  Google Scholar 

  • Salehi S, Ghazizadeh MJ, Tabesh M, Valadi S, Nia SPS (2020) A risk component-based model to determine pipes renewal strategies in water distribution networks. Struct Infrastruct Eng 17(10):1338–1359. https://doi.org/10.1080/15732479.2020.1842466

    Article  Google Scholar 

  • Sattar AM, Gharabaghi B (2015) Gene expression models for prediction of longitudinal dispersion coefficient in streams. J Hydrol 524:587–596

    Article  Google Scholar 

  • Sattar AM, Gharabaghi B, McBean EA (2016) Prediction of timing of watermain failure using gene expression models. Water Resour Manag 30(5):1635–1651

    Article  Google Scholar 

  • Sattar AM, Ertuğrul ÖF, Gharabaghi B, McBean EA, Cao J (2019) Extreme learning machine model for water network management. Neural Comput Appl 31(1):157–169

    Article  Google Scholar 

  • Scholten L, Scheidegger A, Reichert P, Mauer M, Lienert J (2014) Strategic rehabilitation planning of piped water networks using multi-criteria decision analysis. Water Res 49:124–143

    Article  CAS  Google Scholar 

  • Seifollahi-Aghmiuni S, Haddad OB, Mariño MA (2013) Water distribution network risk analysis under simultaneous consumption and roughness uncertainties. Water Resour Manag 27(7):2595–2610

    Article  Google Scholar 

  • Seifollahi-Aghmiuni S, Haddad OB, Omid MH, Mariño MA (2011) Long-term efficiency of water networks with demand uncertainty. Proc Inst Civil Eng Water Manag 164(3):147–159. https://doi.org/10.1680/wama.1000039

    Article  Google Scholar 

  • Shafiqul Islam M, Sadiq R, Rodriguez MJ, Najjaran H, Hoorfar M (2014) Reliability assessment for water supply systems under uncertainties. J Water Resour Plan Manag 140(4):468–479

    Article  Google Scholar 

  • Sharp WW, Walski TM (1988) Predicting internal roughness in water mains. J Am Water Works Ass 80(11):34–40

    Article  Google Scholar 

  • Sheikhmohammady M, Madani K (2008) Bargaining over the Caspian Sea—the largest lake on earth. In: Proceedings of the 2008 World Environmental and Water Resources Congress. ASCE, Honolulu, HI

  • Shin H, Kobayashi K, Koo J, Do M (2015) Estimating burst probability of water pipelines with a competing hazard model. J Hydr 18(1):126–135

    Google Scholar 

  • Shirzad A, Tabesh M, Atayikia B (2017) Multi objective optimization of pressure dependent dynamic design for water distribution networks. Water Resour Manag 31(9):2561–2578

    Article  Google Scholar 

  • Shirzad A, Tabesh M (2016) New indices for reliability assessment of water distribution networks. J Water Supply Res Technol Aqua 65(5):384–395. https://doi.org/10.2166/aqua.2016.091

    Article  Google Scholar 

  • Sivakumar P, Prasad RK (2014) Simulation of water distribution network under pressure-deficient condition. Water Resour Manag 28(10):3271–3290

    Article  Google Scholar 

  • Soltani J, Tabari MMR (2012) Determination of effective parameters in pipe failure rate in water distribution system using the combination of artificial neural networks and genetic algorithm. J Water Wastew 23(83):2–15 (In Persian)

    Google Scholar 

  • Srdjevic B (2006) Linking analytic hierarchy process and social choice methods to support group decision making in water management. Decision Support Syst 42:2261–2273

    Article  Google Scholar 

  • Suribabu CR, Neelakantan TR, Sivakumar P (2017) Improved complementary reservoir solution to evaluate nodal outflow under pressure deficient conditions. ISH J Hyd Eng 23(3):260–266. https://doi.org/10.1080/09715010.2017.1298060

    Article  Google Scholar 

  • Taebi A, Chamani MR (2005) Urban water distribution networks. publication center of Isfahan Industrial University, Isfahan

  • Tavakoli R, Najafi M, Sharifara A (2019) Artificial neural networks and adaptive neuro-fuzzy models for prediction of remaining useful life. arXiv preprint arXiv:1909.02115

  • Thurstone LL (1927) The method of paired comparisons for social values. J Abnorm Soc Psychol 21:384–400

    Article  Google Scholar 

  • Toth E, Bragalli C, Neri M (2018) Assessing the significance of tourism and climate on residential water demand: panel-data analysis and non-linear modelling of monthly water consumptions. Environ Model Softw 103:52–61

    Article  Google Scholar 

  • Triantaphyllou E, Mann S (1995) Using the analytic hierarchy process for decision making in engineering applications: Some challenges. Int J Ind Eng Theory Appl Pract. 2:35–44

    Google Scholar 

  • Trojan F, Morais DC (2012) Prioritizing alternatives for maintenance of water distribution networks: a group decision approach. Water Sa 38(4):555–564

    Article  Google Scholar 

  • Tu MY, Tsai FTC, Yeh WWG (2005) Optimization of water distribution and water quality by hybrid genetic algorithm. J Water Resour Plan Manag 131(6):431–440

    Article  Google Scholar 

  • Vairagade SA, Sayyed, MA, Gupta R (2015) Node head flow relationships in skeletonized water distribution networks for predicting performance under deficient conditions. In World Environmental and Water Resources Congress (pp. 810–819)

  • Vasan A, Simonovic SP (2010) Optimization of water distribution network design using differential evolution. J Water Resour Plan Manag 136(2):279–287

    Article  Google Scholar 

  • Wagner JM, Shamir U, Marks DH (1988) Water distribution reliability: simulation methods. J Water Resour Plan Manag 114(3):276–294

    Article  Google Scholar 

  • Walski TM, Brill ED Jr, Gessler J, Goulter IC, Jeppson RM, Lansey K, Lee HL, Liebman JC, Mays L, Morgan DR, Ormsbee L (1987) Battle of the network models: epilogue. J Water Resour Plan Manag 113(2):191–203

    Article  Google Scholar 

  • Wang Q, Guidolin M, Savic D, Kapelan Z (2015) Two-objective design of benchmark problems of a water distribution system via MOEAs: Towards the best-known approximation of the true Pareto front. J Water Resour Plan Manag 141(3):04014060

    Article  Google Scholar 

  • Wu Z, Abdul-Nour G (2020) Comparison of multi-criteria group decision-making methods for urban sewer network plan selection. CivilEng 1(1):26–48

    Article  Google Scholar 

  • Xu Q, Chen Q, Li W, Ma J (2011) Pipe break prediction based on evolutionary data-driven methods with brief recorded data. Reliab Eng Syst Saf 96(8):942–948

    Article  Google Scholar 

  • Xu Q, Chen Q, Ma J, Blanckaert K (2013) Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. J Hydr Environ Res 7(2):134–140

    Article  CAS  Google Scholar 

  • Zhou H, Wang JQ, Zhang HY (2018) Multi-criteria decision-making approaches based on distance measures for linguistic hesitant fuzzy sets. J Oper Res Soc 69(5):661–675

    Article  Google Scholar 

  • Zubaidi SL, Dooley J, Alkhaddar RM, Abdellatif M, Al-Bugharbee H, Ortega-Martorell S (2018) A novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks. J Hydrol 561:136–145

    Article  Google Scholar 

  • Zubaidi SL, Ortega-Martorell S, Kot P, Alkhaddar RM, Abdellatif M, Gharghan SK, Hashim K (2020) A method for predicting long-term municipal water demands under climate change. Water Resour Manag 34(3):1265–1279

    Article  Google Scholar 

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Acknowledgements

I would like to thank the Water and Sewerage Company of Golestan Province for cooperation in conducting this research.

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Correspondence to A. Zahiri.

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Jafari, S.M., Zahiri, A., Bozorg-Haddad, O. et al. Development of multi-objective optimization model for water distribution network using a new reliability index. Int. J. Environ. Sci. Technol. 19, 9757–9774 (2022). https://doi.org/10.1007/s13762-022-04171-2

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