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• Ahn H, Clermont M, Langner J (2023) Comparative performance analysis of frontier-based efficiency measurement methods—a Monte Carlo simulation. Eur J Oper Res 307(1):294–312

  Article  Google Scholar 

• Bilici S, Kamislioglu M, Guclu EEA (2023) A Monte Carlo simulation study on the evaluation of radiation protection properties of spectacle lens materials. Eur Phys J plus 138(1):80

  Article  CAS  Google Scholar 

• Blocher C, Pecci F, Stoianov I (2020) Localizing leakage hotspots in water distribution networks via the regularization of an inverse problem. J Hydraul Eng 146(4):04020025

  Article  Google Scholar 

• Bozorg Haddad O, Seifollahi-Aghmiuni S (2013) An introduction to uncertainty analysis in water resources system, University of Tehran Press

• Cugueró-Escofet P, Blesa J, Pérez R et al (2015) Assessment of a leak localization algorithm in water networks under demand uncertainty. IFAC-PapersOnLine 48(21):226–231

  Article  Google Scholar 

• Eliades DG, Kyriakou M, Vrachimis S et al. (2016) EPANET-MATLAB Toolkit: An open-source software for interfacing EPANET with MATLAB. In: 14th International Conference on Computing and Control for the Water Industry (CCWI), Amsterdam, The Netherlands, 7–9

• El-Sharkawy YH, Elbasuney S, Radwan SM (2024) Non-invasive diffused reflected/transmitted signature accompanied with hyperspectral imaging for breast cancer early diagnosis. Opt Laser Technol 169:110151

  Article  CAS  Google Scholar 

• Faghfoor Maghrebi M, Hasanzadeh Y, Yazdani S (2013) Calibration of water supply systems based on ant colony optimization (in Persian). J Water Wastewater Ab Va Fazilab (in Persian) 24(1):101–111

  Google Scholar 

• Fallahi H, Jalili Ghazizadeh M, Aminnejad B et al (2021) Leakage detection in water distribution networks using hybrid feedforward artificial neural networks. AQUA—Water Infrastruct Ecosyst Soc 70(5):637–653

  Google Scholar 

• Govender P, Sivakumar V (2020) Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980–2019). Atmos Pollut Res 11(1):40–56

  Article  CAS  Google Scholar 

• Hamilton S, Charalambous B (2020) Leak Detection: Technology and Implementation, IWA Publishing

• Hayashi T, Cimr D, Studnička F et al (2024) Distance-based one-class time-series classification approach using local cluster balance. Expert Syst Appl 235:121201

  Article  Google Scholar 

• Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology. MIT Press, Control and Artificial Intelligence

  Book  Google Scholar 

• Hu X, Han Y, Yu B et al (2021) el leakage detection and water loss management of urban water supply network using multiscale neural networks. J Clean Prod 278:123611

  Article  Google Scholar 

• Ikotun AM, Ezugwu AE, Abualigah L et al (2023) K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf Sci 622:178–210

  Article  Google Scholar 

• Javidan SM, Banakar A, Vakilian KA et al (2023) Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agric Technol 3:100081

  Article  Google Scholar 

• Kanakoudis V, Tsitsifli S (2012) Urban water services public infrastructure projects: turning the high level of the NRW into an attractive financing opportunity using the PBSC tool. Desalin Water Treat 39(1–3):323–335

  Article  Google Scholar 

• Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multim Tools Appl 80(5):8091–8126

  Article  Google Scholar 

• Kodinariya T, Makwana P (2013) Review on determining of cluster in K-means clustering. Int J Adv Res Comput Sci Manag Stud 1:90–95

  Google Scholar 

• Lambert A n.d. What do we know about pressure-leakage relationships in distribution systems. In: Proceedings of the IWA Conference on System Approach to Leakage Control and Water Distribution System Management, Brno, Czech Republic

• Lipowski A, Lipowska D (2012) Roulette-wheel selection via stochastic acceptance. Physica A 391(6):2193–2196

  Article  Google Scholar 

• Marzola I, Mazzoni F, Alvisi S et al (2022) Leakage detection and localization in a water distribution network through comparison of observed and simulated pressure data. J Water Resour Plan Manag 148(1):04021096

  Article  Google Scholar 

• Moasheri R, Jalili-Ghazizadeh M (2020) Locating of probabilistic leakage areas in water distribution networks by a calibration method using the imperialist competitive algorithm. Water Resour Manage 34(1):35–49

  Article  Google Scholar 

• Moasheri R, Jalili Ghazizadeh M, Tashayoei M (2021) Leakage detection in water networks by a calibration method. Flow Meas Instrum 80:101995

  Article  Google Scholar 

• Mounce S, Boxall J, Machell J (2010) Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows. J Water Resour Plan Manag 136(3):309–318

  Article  Google Scholar 

• Nasirian A, Maghrebi M, Yazdani S (2013) Leakage detection in water distribution network based on a new heuristic genetic algorithm model. J Water Resour Prot 05:294–303

  Article  Google Scholar 

• Pérez-Pérez EJ, López-Estrada FR, Valencia-Palomo G et al (2021) Leak diagnosis in pipelines using a combined artificial neural network approach. Control Eng Pract 107:104677

  Article  Google Scholar 

• Poulakis Z, Valougeorgis D, Papadimitriou C (2003) Leakage detection in water pipe networks using a Bayesian probabilistic framework. Probab Eng Mech 18(4):315–327

  Article  Google Scholar 

• Reca J, Martínez J (2006) Genetic algorithms for the design of looped irrigation water distribution networks. Water Resour Res 42(5).

• Sadr-Al-Sadati SA, Jalili Ghazizadeh M (2019) The experimental and numerical study of water leakage from high-density. Polym Testing 74:274–280

  Article  CAS  Google Scholar 

• Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

  Article  Google Scholar 

• Setiawan KE, Kurniawan A, Chowanda A et al (2023) Clustering models for hospitals in Jakarta using fuzzy c-means and k-means. Procedia Comput Sci 216:356–363

  Article  Google Scholar 

• Snider B, Lewis G, Chen A et al. (2023) A flexible, leak crew focused localization model using a maximum coverage search area algorithm. In: 14th International Conference on Hydroinformatics, IOP Publishing

• Sousa J, Ribeiro L, Muranho J et al (2015) Locating leaks in water distribution networks with simulated annealing and graph theory. Procedia Eng 119:63–71

  Article  Google Scholar 

• Tijani IA, Abdelmageed S, Fares A et al (2022) Improving the leak detection efficiency in water distribution networks using noise loggers. Sci Total Environ 821:153530

  Article  CAS  Google Scholar 

• Wachla D, Przystalka P, Moczulski W (2015) A method of leakage location in water distribution networks using artificial neuro-fuzzy system. IFAC-PapersOnLine 48(21):1216–1223

  Article  Google Scholar 

• Wang Z, Hou S, Guo W (2024) Inventory management of battery swapping and charging stations considering uncertainty. Int J Electr Power Energy Syst 155:109528

  Article  Google Scholar 

• Wang L, Xiao T, Liu S et al. (2023) Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation. Gondwana Res

• Wiroonsri N (2024) Clustering performance analysis using a new correlation-based cluster validity index. Pattern Recogn 145:109910

  Article  Google Scholar 

• Wu Z, Sage P, Turtle D et al (2009) Leak detection case study by means of optimizing emitter locations and flows. Water Distrib Syst Anal 2008:1–11

  Google Scholar 

• Wu J, Ma D, Wang W (2022) Leakage identification in water distribution networks based on XGBoost algorithm. J Water Resour Plan Manag 148(3):04021107

  Article  Google Scholar 

• Zaman D, Tiwari MK, Gupta AK et al (2020) A review of leakage detection strategies for pressurised pipeline in steady-state. Eng Fail Anal 109:104264

  Article  Google Scholar 

• Zhao SL, Liu S-G, Qiu B et al (2023) Leak detection method of liquid-filled pipeline based on VMD and SVM. Urban Water J 20(9):1169–1182

  Article  CAS  Google Scholar 

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Authors and Affiliations

1. Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

   R. Moasheri, M. Jalili Ghazizadeh & R. Ahmadi Kohanali

Contributions

Reza Moasheri: Methodology, Software, Writing—Original Draft, Formal analysis, Investigation; Mohammadreza Jalili Ghazizadeh: Supervision, Conceptualization, Validation, Resources, Writing—Review & Editing, Data Curation; Reza Ahmadi Kohanali: Methodology, Software, Writing—Original Draft, Formal analysis, Investigation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to M. Jalili Ghazizadeh.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This paper does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication

All authors have read and approved the final manuscript to be published.

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