Treffer: Resilient Simulation and Optimization of Water Distribution Networks Using WNTR and Python: Hydraulic Design and Synthetic Data Generation for Scenario Analysis ; Simulación y optimización resiliente de redes de distribución de agua potable mediante WNTR y Python: diseño hidráulico y generación de datos sintéticos para análisis de escenarios

Title:
Resilient Simulation and Optimization of Water Distribution Networks Using WNTR and Python: Hydraulic Design and Synthetic Data Generation for Scenario Analysis ; Simulación y optimización resiliente de redes de distribución de agua potable mediante WNTR y Python: diseño hidráulico y generación de datos sintéticos para análisis de escenarios
Source:
Revista Respuestas; ##issue.vol## 30 ##issue.no## 3 (2025): SEPTIEMBRE - DICIEMBRE 2025; 22-44 ; Respuestas; Vol. 30 Núm. 3 (2025): SEPTIEMBRE - DICIEMBRE 2025; 22-44 ; 2422-5053 ; 0122-820X
Publisher Information:
Universidad Francisco de Paula Santander
Publication Year:
2025
Collection:
Portal de Revistas UFPS (Revistas Institucionales Universidad Francisco de Paula Santander)
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
Spanish; Castilian
Relation:
https://revistas.ufps.edu.co/index.php/respuestas/article/view/5342/6282; I. Arias et al., “Smart Data Analysis for Smart Water Networks,” Congreso de Métodos Numéricos en Ingeniería (CMN 2017). Actas, pp. 1665–1677, Jul. 2017, Accessed: Jan. 28, 2024. [Online]. Available: https://riunet.upv.es/handle/10251/179917 [2] M. Gómez et al., “Water Supply Management Index: Leon, Guanajuato, Mexico,” Water 2022, Vol. 14, Page 919, vol. 14, no. 6, p. 919, Mar. 2022, doi:10.3390/W14060919. [3] I. Figueiredo, P. Esteves, and P. Cabrita, “Water wise – a digital water solution for smart cities and water management entities,” Procedia Comput Sci, vol. 181, pp. 897–904, Jan. 2021, doi:10.1016/J.PROCS.2021.01.245. [4] L. A. Rossman, H. Woo, M. Tryby, F. Shang, and R. Janke, Manual del usuario de EPANET 2.2. EPA, US Environmental Protection Agency, 2002. [5] C. Alexis Bonilla-Granados et al., “Digitalización de redes de distribución de agua, implementando imágenes satelitales, drones y sistemas de información geográfica,” Respuestas, vol. 28, no. 3, pp. 22–38, Sep. 2023, doi:10.22463/0122820X.4156. [6] E. Todini, “Looped water distribution networks design using a resilience index based heuristic approach,” Urban Water, vol. 2, no. 2, pp. 115–122, Jun. 2000, doi:10.1016/S1462-0758(00)00049-2. [7] K. A. Klise, R. Murray, and T. Haxton, “An overview of the Water Network Tool for Resilience (WNTR).,” in 1st International WDSA / CCWI 2018 Joint Conference, Kingston, Ontario, Canada: Queen’s University and the Royal Military College, Jul. 2018, pp. 1–8. Accessed: Jan. 28, 2024. [Online]. Available: https://www.osti.gov/servlets/purl/1510389 [8] S. Parvaze et al., “Optimization of Water Distribution Systems Using Genetic Algorithms: A Review,” Archives of Computational Methods in Engineering 2023 30:7, vol. 30, no. 7, pp. 4209–4244, May 2023, doi:10.1007/S11831-023-09944-7. [9] A. Javed, W. Wu, Q. Sun, and Z. Dai, “Leak Management in Water Distribution Networks Through Deep Reinforcement Learning: A Review,” Water 2025, Vol. 17, Page 1928, vol. 17, no. 13, p. 1928, Jun. 2025, doi:10.3390/W17131928. [10] T. Tunque-Dueñas, F. Ricra-Dueñas, I. Ayala, E. Contreras-Lopez, and M. Portuguez-Maurtua, “Aplicación del algoritmo multiobjetivo NSGA-II en el diseño óptimo de redes de distribución de agua potable. Caso: ciudad de Huancavelica, Perú,” Tecnología y ciencias del agua, vol. 16, no. 2, pp. 168–211, Mar. 2025, doi:10.24850/J-TYCA-2025-02-04. [11] H. F. Duan, B. Pan, M. Wang, L. Chen, F. Zheng, and Y. Zhang, “State-of-the-art review on the transient flow modeling and utilization for urban water supply system (UWSS) management,” Journal of Water Supply: Research and Technology-Aqua, vol. 69, no. 8, pp. 858–893, Dec. 2020, doi:10.2166/AQUA.2020.048. [12] H. M. Ramos et al., “New Challenges towards Smart Systems’ Efficiency by Digital Twin in Water Distribution Networks,” Water 2022, Vol. 14, Page 1304, vol. 14, no. 8, p. 1304, Apr. 2022, doi:10.3390/W14081304. [13] J. Carneiro, D. Loureiro, and D. Covas, “Exploratory Analysis of Surrogate Metrics to Assess the Resilience of Water Distribution Networks,” Water Resour Res, vol. 59, no. 8, p. e2022WR034289, Aug. 2023, doi:10.1029/2022WR034289;PAGE:STRING:ARTICLE/CHAPTER. [14] J. Liu, Z. Shao, and W. Wang, “Resilience Assessment and Critical Point Identification for Urban Water Supply Systems under Uncertain Scenarios,” Water 2021, Vol. 13, Page 2939, vol. 13, no. 20, p. 2939, Oct. 2021, doi:10.3390/W13202939. [15] K.-H.; Han et al., “Optimization of Water Distribution Networks Using Genetic Algorithm Based SOP–WDN Program,” Water 2022, Vol. 14, Page 851, vol. 14, no. 6, p. 851, Mar. 2022, doi:10.3390/W14060851. [16] C. A. Bonilla, B. Brentan, I. Montalvo, D. Ayala-Cabrera, and J. Izquierdo, “Assessing the Impacts of Failures on Monitoring Systems in Real-Time Data-Driven State Estimation Models Using GCN-LSTM for Water Distribution Networks,” Water 2025, Vol. 17, Page 46, vol. 17, no. 1, p. 46, Dec. 2024, doi:10.3390/W17010046. [17] C. A.; Bonilla et al., “A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation,” Water 2022, Vol. 14, Page 514, vol. 14, no. 4, p. 514, Feb. 2022, doi:10.3390/W14040514. [18] P. Conejos Fuertes, F. Martínez Alzamora, M. Hervás Carot, and J. C. Alonso Campos, “Building and exploiting a Digital Twin for the management of drinking water distribution networks,” Urban Water J, vol. 17, no. 8, pp. 704–713, Sep. 2020, doi:10.1080/1573062X.2020.1771382. [19] S. Díaz, R. Mínguez, and J. González, “Calibration via Multi-period State Estimation in Water Distribution Systems,” Water Resources Management, vol. 31, no. 15, pp. 4801–4819, Dec. 2017, doi:10.1007/S11269-017-1779-2/METRICS. [20] C. y T. R. de C. Ministerio de Vivienda, “Reglamento Técnico para el Sector Agua Potable y Saneamiento Básico – RAS, Resolución 0330 - 2017,” Bogotá, Colombia, Jun. 2017. Accessed: Sep. 24, 2023. [Online]. Available: https://minvivienda.gov.co/normativa/resolucion-0330-2017-0 [21] J. Kim and S. Yoo, “Software review: DEAP (Distributed Evolutionary Algorithm in Python) library,” Genet Program Evolvable Mach, vol. 20, no. 1, pp. 139–142, Mar. 2019, doi:10.1007/S10710-018-9341-4/FIGURES/1.; https://revistas.ufps.edu.co/index.php/respuestas/article/view/5342
DOI:
10.22463/0122820X.5342
Rights:
Derechos de autor 2025 Respuestas ; https://creativecommons.org/licenses/by-nc/4.0
Accession Number:
edsbas.EB27D19F
Database:
BASE

Weitere Informationen

Resilient design of water distribution networks (WDNs) is a critical challenge in contemporary hydraulic engineering. This article presents a simulation and optimization methodology based on Python, the WNTR (Water Network Tool for Resilience) library, and EPANET, aimed at developing hydraulic models that comply with Colombian regulations, enhance system performance, and increase the network’s capacity to respond to failures. The proposed approach includes the calculation of Todini’s resilience index, the automated design of physical parameters, an extended-period simulation-based validation, and a subsequent optimization using genetic algorithms to improve the cost-pressure relationship in hydraulic design. Furthermore, a Python-based simulator was developed to generate synthetic time series of pressure and flow, as well as to evaluate scenarios involving leaks, pipe closures, and demand variations. The methodology was applied to two case studies: Red-Net1 (theoretical) and Red Primavera (real), demonstrating improvements in hydraulic performance, reduced computational time, and compliance with normative criteria. The results confirm that the proposed framework constitutes a robust tool for the design, analysis, and data generation of water distribution systems. ; El diseño resiliente de redes de distribución de agua potable (RDAP) es un reto clave en la ingeniería hidráulica actual. Este artículo presenta una metodología de simulación y optimización basada en Python, la librería WNTR (Water Network Tool for Resilience) y EPANET, con el objetivo de obtener modelos hidráulicos que cumplan con la normativa colombiana, mejoren el desempeño del sistema y aumenten su capacidad de respuesta ante fallos. La propuesta incluye el cálculo del índice de resiliencia de Todini, el diseño automatizado de parámetros físicos, una validación con simulaciones extendidas y una optimización posterior basada en algoritmos genéticos para mejorar la relación costo-presión en el diseño hidráulico. Además, se desarrolló un simulador en ...