Digitalization of Water Distribution Systems in Small Cities, a Tool for Verification and Hydraulic Analysis: A Case Study of Pamplona, Colombia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Analysis of Existing Information
- Documentary record: In the government offices (technical offices in charge of the infrastructure in each city), physical and digital layouts should be consulted, as well as technical documents related to the land registry of the WDS. Similarly, existing WDS layouts in the water company’s technical offices should be found and compiled.
- Urban layouts: The most up-to-date layouts of the city, urban roads, road infrastructure, and land use maps should be obtained from public offices.
- Satellite images: Currently, there is recent, updated, and free-access satellite information that shows the development and urban growth of each community. It is essential to use the existing edited images of the study site and contrast them with the city’s physical and digital maps. With these tools, it is possible to identify new neighborhoods (e.g., recent urban developments or unplanned neighborhoods made up of low-income or immigrant families on the outskirts of the city) or non-registered urban settlements in the existing maps. This information should be included and updated.
- Tacit information: Once the existing information is verified and analyzed, field visits should be carried out, preferably with the water utility experts to know the operation of the WDS. The most experienced active personnel in water companies have excellent information in their memory and this information is called tacit information. This type of information is valuable to contrast the information obtained via documentary records, urban layouts, and satellite images with. Fieldwork helps to identify the visible components of the system (valves, reservoirs, reservoirs, pumping stations, hydrants, and sensors) and their physical properties (dimensions, diameters, and materials) to complement the missing information. If necessary, field inspections should be conducted using boreholes to obtain any missing information. Subsequently, this information should be digitized and reflected in an initial layout of the WDS.
- Preliminary digitization: Using the collected information, a preliminary digitization of the WDS is carried out using a computer-aided design (CAD); alternatively, geographic information systems (GIS) can also be used in this activity. It is essential to use several layers in the drawing process to identify and classify the different materials and diameters of the pipes and the existing elements in the network (valves, tanks, and hydrants). This process allows us to roughly understand the general structure of the WDS and to get to know the most important pipes and elements in the network.
2.2. Conceptualization of the Hydraulic Model
- Continuity of service: This is determined to know the actual time of service offered by the system during the day, to know if the service is continuous or intermittent, and in the case of the intermittent systems, to identify the service shifts and the areas supplied in the different shifts that may exist. This information is relevant to determining consumption patterns and should be consulted with the water company’s experts; if possible, the company’s records of pipe damage and repair times should be consulted.
- Pipe information: It is necessary to know the diameter of each pipe, length, type of material, and approximate age. Knowing the age allows for the establishment of an approximate roughness coefficient, which is necessary for the loss equations of the hydraulic model.
- Network fittings: These comprise the location, diameter, and material of strategic valves used by workers to operate the network, as well as pressure regulating valves, need to be identified. Hydrants and other relevant accessories for hydraulic operation should also be identified.
- Storage tanks: It is essential to know the existing tanks, their location, volume, and internal dimensions, as well as the variation in water levels throughout the day. It is also necessary to have an idea of the supply areas of each tank.
- Pumping equipment (if any): The pumping stations that operate in the network must be identified; it is necessary to have clarity on the number of pumps installed, power characteristics, and models installed to determine the operating curve of each pump and to know the suction and discharge pipes.
- District metered areas: If there are metered district areas in the WDS where flow or pressure data are available, they should be detailed and similarly shown in the network plans and in the hydraulic model to be built. If there is no sectorization, the company’s experts should be consulted on how the network is operated, whether it supplies all users continuously and without district zones, or whether there are service shifts in defined areas determined on an experimental basis with valve management. Intermittent service provision should be analyzed based on a thorough knowledge of intermittent water distribution, as recommended [54].
- Monitoring data: The existence of measured data on pressure, flow, and water quality in the network should be investigated. These data can help understand the behavior of the network and will be used to perform the calibration process. In water utilities that do not have this information, it is recommended to implement monitoring campaigns—preferably pressure and flow—at some points of the network and according to the limited budget that can be allocated for these activities.
2.3. Drone Assistance
- Flight planning: The areas of interest must be identified, delimiting the general perimeter of the location from which information will be obtained. Mobile applications (e.g., DJI GO o PIX4Dcapture Pro) are used to plan the number of flights to be carried out, called missions, according to the size of the total area. Flights should be made on sunny days; the drone should not be flown on rainy, cloudy, or foggy days, as this affects the quality of the images and the drone’s reception signal.
- Configuration of flight parameters: Flight altitude, maximum flight time, and photo capture interval are configured. These parameters depend on aspects such as the height of the buildings or existing infrastructure in the city (telephone towers, electric power antennas, etc.) and the state of the batteries that limits the maximum flight time per mission.
- Image acquisition and processing: Planned flights are carried out to obtain photographs. The information taken by the drone is downloaded to a computer and the quality of images is verified by checking that there are no blurred or distorted photographs due to clouds or any external element around the drone. Subsequently, the images must be processed to obtain the sense and orientation of each picture, which is carried out by analyzing the pixels of each image and the similarity of these pixels in the other photographs to obtain the overall picture of the area. Finally, 3D spatial data are generated, with points containing geographic and elevation information. There are different tools to analyze the photographs taken with a drone. In this case, the Agisoft Metashape photogrammetric application [56] is used to process and orient the images based on the GPS information of the drone and the relative position in each mission. It is necessary to verify that all pictures have information to guarantee the quality of the result. If, due to signal reception problems (e.g., a loss of connection with GPS satellites or interference with radio or mobile phone signals), information is not obtained from any area, a new mission must be planned for that sector.
- Checkpoints: With the information of the national geodetic network “https://redgeodesica.igac.gov.co/redes/red_geodesica.html (accessed on 17 January 2023)”, the existing georeferenced points within the flight area must be located and the elevation of the points and their coordinates, at least, must be obtained, as well as the geodetic coordinate system in which they are located. If these points are unavailable, a high-precision GPS should be used to obtain that information. This information will minimize the error and increase the accuracy of the elevation model obtained.
- Creation of the point cloud: The following process consists of creating the point cloud, which allows for the identification of points with the same pixel information in the photographs and the construction of a first 3D image with the data captured with the drone. From this, the mesh is created, which is the base element with which to obtain elevations of the model; it is recommended to work at a high resolution (a face count parameter equal to 180,000). It is necessary to carry out the process of classification of points to purge elements that are not of interest in the model to be generated.
- Digital elevation model (DEM): Finally, processing is performed to obtain the DEM, contour lines, and orthophoto. These three products can be exported in independent files in shapefile, DWG, or JPG format, among others, so that they can be visualized in software such as AutoCAD, QGIS, or ArcGIS.
2.4. Consumption Analysis and Demand Pattern
2.5. Hydraulic Model, Hydraulic Analysis, and Non-Revenue Water Basic Index
2.6. Study Location
3. Results
3.1. Existing Data and Conceptualization of the Hydraulic Model in Pamplona
3.2. Drone Assistance
3.3. Consumption Analysis and Demand Pattern
3.4. Building of the Model for Hydraulic Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Makropoulos, C.; Savić, D.A.S.; Nl, C.M. Urban Hydroinformatics: Past, Present and Future. Water 2019, 11, 1959. [Google Scholar] [CrossRef]
- Gómez, M.; Tagle-Zamora, M.; Morales Martínez, D.; Caldera Ortega, J.L.; Mora Rodríguez, A.R.; Delgado-Galván, J.D.J.; Mendoza Gómez, M.; Tagle-Zamora, D.; Morales Martínez, J.L.; Caldera Ortega, A.R.; et al. Water Supply Management Index: Leon, Guanajuato, Mexico. Water 2022, 14, 919. [Google Scholar] [CrossRef]
- Naciones Unidas. La Agenda 2030 y Los Objetivos de Desarrollo Sostenible: Una Oportunidad Para América Latina y El Caribe. Objetivos, Metas e Indicadores Mundiales; Naciones Unidas: Santiago, Chile, 2019. [Google Scholar]
- Benedict, S.; Hussein, H. An Analysis of Water Awareness Campaign Messaging in the Case of Jordan: Water Conservation for State Security. Water 2019, 11, 1156. [Google Scholar] [CrossRef]
- Ortega-Ballesteros, A.; Manzano-Agugliaro, F.; Perea-Moreno, A.J. Water Utilities Challenges: A Bibliometric Analysis. Sustainability 2021, 13, 7726. [Google Scholar] [CrossRef]
- Snider, B.; McBean, E.A. Watermain Breaks and Data: The Intricate Relationship between Data Availability and Accuracy of Predictions. Urban Water J. 2020, 17, 163–176. [Google Scholar] [CrossRef]
- Pingale, S.M.; Jat, M.K.; Khare, D. Integrated Urban Water Management Modelling under Climate Change Scenarios. Resour. Conserv. Recycl. 2014, 83, 176–189. [Google Scholar] [CrossRef]
- Figueiredo, I.; Esteves, P.; Cabrita, P. Water Wise—A Digital Water Solution for Smart Cities and Water Management Entities. Procedia Comput. Sci. 2021, 181, 897–904. [Google Scholar] [CrossRef]
- Helmbrecht, J. Transformación Digital de Redes Inteligentes: Retos y Riesgos. Tecnoaqua 2018, 32, 84–86. [Google Scholar]
- Shende, S.; Chau, K.W. Design of Water Distribution Systems Using an Intelligent Simple Benchmarking Algorithm with Respect to Cost Optimization and Computational Efficiency. Water Supply 2019, 19, 1892–1898. [Google Scholar] [CrossRef]
- Liu, A.; Mukheibir, P. Digital Metering Feedback and Changes in Water Consumption—A Review. Resour. Conserv. Recycl. 2018, 134, 136–148. [Google Scholar] [CrossRef]
- Bharani Baanu, B.; Jinesh Babu, K.S. Smart Water Grid: A Review and a Suggestion for Water Quality Monitoring. Water Supply 2022, 22, 1434–1444. [Google Scholar] [CrossRef]
- Gupta, A.D.; Kulat, K. Leakage Reduction in Water Distribution System Using Efficient Pressure Management Techniques. Case Study: Nagpur, India. Water Supply 2018, 18, 2015–2027. [Google Scholar] [CrossRef]
- Menapace, A.; Zanfei, A.; Felicetti, M.; Avesani, D.; Righetti, M.; Gargano, R. Burst Detection in Water Distribution Systems: The Issue of Dataset Collection. Appl. Sci. 2020, 10, 8219. [Google Scholar] [CrossRef]
- Quintiliani, C.; Marquez-Calvo, O.; Alfonso, L.; Cristo, C.D.; Leopardi, A.; Solomatine, D.P.; Marinis, G. de Multiobjective Valve Management Optimization Formulations for Water Quality Enhancement in Water Distribution Networks. J. Water Resour. Plan. Manag. 2019, 145, 04019061. [Google Scholar] [CrossRef]
- Quintiliani, C.; Di Cristo, C.; Leopardi, A. Vulnerability Assessment to Trihalomethane Exposure in Water Distribution Systems. Water 2018, 10, 912. [Google Scholar] [CrossRef]
- Ramos, H.M.; Morani, M.C.; Carravetta, A.; Fecarrotta, O.; Adeyeye, K.; López-Jiménez, P.A.; Pérez-Sánchez, M. New Challenges towards Smart Systems’ Efficiency by Digital Twin in Water Distribution Networks. Water 2022, 14, 1304. [Google Scholar] [CrossRef]
- Conejos Fuertes, P.; Martínez Alzamora, F.; Hervás Carot, M.; Alonso Campos, J.C. Building and Exploiting a Digital Twin for the Management of Drinking Water Distribution Networks. Urban Water J. 2020, 17, 704–713. [Google Scholar] [CrossRef]
- Erena, M.; Atenza, J.F.; García-Galiano, S.; Domínguez, J.A.; Bernabé, J.M. Use of Drones for the Topo-Bathymetric Monitoring of the Reservoirs of the Segura River Basin. Water 2019, 11, 445. [Google Scholar] [CrossRef]
- Hu, R.Y. Sources and Routes from Terrestrial Exogenous Pollutants Affect Phytoplankton Biomass in Reservoir Bays. Water Supply 2021, 21, 3913–3931. [Google Scholar] [CrossRef]
- Stanford, B.D.; Pochiraju, S.; Yokoyama, T.; Maari, P.; Grijalva, L.; Bukhari, Z. Evaluating Satellite and in Situ Monitoring Technologies for Leak Detection and Response. AWWA Water Sci. 2022, 4, e1288. [Google Scholar] [CrossRef]
- Sadowski, B. Book Review: A Modern Guide to the Digitalization of Infrastructure. Digit. Policy Regul. Gov. 2022, 24, 220–224. [Google Scholar] [CrossRef]
- Adedeji, K.B.; Ponnle, A.A.; Abu-Mahfouz, A.M.; Kurien, A.M. Towards Digitalization of Water Supply Systems for Sustainable Smart City Development—Water 4.0. Appl. Sci. 2022, 12, 9174. [Google Scholar] [CrossRef]
- Balogun, A.L.; Marks, D.; Sharma, R.; Shekhar, H.; Balmes, C.; Maheng, D.; Arshad, A.; Salehi, P. Assessing the Potentials of Digitalization as a Tool for Climate Change Adaptation and Sustainable Development in Urban Centres. Sustain. Cities Soc. 2020, 53, 101888. [Google Scholar] [CrossRef]
- Ceipek, R.; Hautz, J.; Petruzzelli, A.M.; De Massis, A.; Matzler, K. A Motivation and Ability Perspective on Engagement in Emerging Digital Technologies: The Case of Internet of Things Solutions. Long. Range Plan. 2021, 54, 101991. [Google Scholar] [CrossRef]
- Mondejar, M.E.; Avtar, R.; Diaz, H.L.B.; Dubey, R.K.; Esteban, J.; Gómez-Morales, A.; Hallam, B.; Mbungu, N.T.; Okolo, C.C.; Prasad, K.A.; et al. Digitalization to Achieve Sustainable Development Goals: Steps towards a Smart Green Planet. Sci. Total Environ. 2021, 794, 148539. [Google Scholar] [CrossRef]
- Sun, C.; Cembrano, G.; Puig, V.; Meseguer, J. Cyber-Physical Systems for Real-Time Management in the Urban Water Cycle. In Proceedings of the 2018 4th International Workshop on Cyber-Physical Systems for Smart Water Networks, CySWater, Porto, Portugal, 10–13 April 2018; pp. 5–8. [Google Scholar] [CrossRef]
- Sun, C.; Puig, V.; Cembrano, G. Real-Time Control of Urban Water Cycle under Cyber-Physical Systems Framework. Water 2020, 12, 406. [Google Scholar] [CrossRef]
- Bhardwaj, J.; Gupta, K.K.; Gupta, R. Towards a Cyber-Physical Era: Soft Computing Framework Based Multi-Sensor Array for Water Quality Monitoring. Drink. Water Eng. Sci. 2018, 11, 9–17. [Google Scholar] [CrossRef]
- Chalh, R.; Bakkoury, Z.; Ouazar, D.; Hasnaoui, M.D. Big Data Open Platform for Water Resources Management. In Proceedings of the 2015 International Conference on Cloud Computing Technologies and Applications, CloudTech, Marrakech, Morocco, 2–4 June 2015. [Google Scholar] [CrossRef]
- Moumen, A.; Aghoutane, B.; Lakhrissi, Y.; Essahlaoui, A. Big Data Architecture for Moroccan Water Stakeholders: Proposal and Perception. Lect. Notes Electr. Eng. 2022, 745, 241–246. [Google Scholar]
- Ahirvar, B.P.; Panday, S.; Das, P. Water Indices: Specification, Criteria, and Applications—A Case Study. In Resilience, Response, and Risk in Water Systems; Springer: Singapore, 2020; pp. 73–102. [Google Scholar] [CrossRef]
- Shayan, M.N.M.; Tushara Chaminda, G.G.; Ellawala, K.C.; Gunawardena, W.B. Evaluation of Water Quality of Community Managed Water Supply Schemes (CMWSS) in Galle District. In Resilience, Response, and Risk in Water Systems; Springer: Singapore, 2020; pp. 139–150. [Google Scholar] [CrossRef]
- Candelieri, A.; Archetti, F. Smart Water in Urban Distribution Networks: Limited Financial Capacity And Big Data Analytics. WIT Trans. Built Environ. 2014, 139, 63–73. [Google Scholar] [CrossRef]
- Meseguer, J.; Mirats-Tur, J.M.; Cembrano, G.; Puig, V.; Quevedo, J.; Pérez, R.; Sanz, G.; Ibarra, D. A Decision Support System for On-Line Leakage Localization. Environ. Model. Softw. 2014, 60, 331–345. [Google Scholar] [CrossRef]
- Vegas Niño, O.T.; Martínez Alzamora, F.; Tzatchkov, V.G. A Decision Support Tool for Water Supply System Decentralization via Distribution Network Sectorization. Processes 2021, 9, 642. [Google Scholar] [CrossRef]
- Armon, A.; Gutner, S.; Rosenberg, A.; Scolnicov, H. Algorithmic Network Monitoring for a Modern Water Utility: A Case Study in Jerusalem. Water Sci. Technol. 2011, 63, 233–239. [Google Scholar] [CrossRef] [PubMed]
- Wu, Z.Y.; Sage, P. Water Loss Detection via Genetic Algorithm Optimization-Based Model Calibration. In Proceedings of the 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, OH, USA, 27–30 August 2006; pp. 1–11. [Google Scholar] [CrossRef]
- Mamlook, R.; Al-Jayyousi, O. Fuzzy Sets Analysis for Leak Detection in Infrastructure Systems: A Proposed Methodology. Clean. Technol. Environ. Policy 2003, 6, 26–31. [Google Scholar] [CrossRef]
- Candelieri, A. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water 2017, 9, 224. [Google Scholar] [CrossRef]
- Venkateswaran, P.; Suresh, M.A.; Venkatasubramanian, N. Augmenting In-Situ with Mobile Sensing for Adaptive Monitoring of Water Distribution Networks. In Proceedings of the ICCPS 2019—2019 ACM/IEEE International Conference on Cyber-Physical Systems, Montreal, QC, Canada, 16–18 April 2019; pp. 151–162. [Google Scholar] [CrossRef]
- Afifi, M.; Abdelkader, M.F.; Ghoneim, A. An IoT System for Continuous Monitoring and Burst Detection in Intermittent Water Distribution Networks. In Proceedings of the 2018 International Conference on Innovative Trends in Computer Engineering, ITCE, Aswan, Egypt, 19–21 February 2018; pp. 240–247. [Google Scholar] [CrossRef]
- Pérez-Padillo, J.; Morillo, J.G.; Ramirez-Faz, J.; Roldán, M.T.; Montesinos, P. Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks. Sensors 2020, 20, 4247. [Google Scholar] [CrossRef]
- Maroli, A.A.; Narwane, V.S.; Raut, R.D.; Narkhede, B.E. Framework for the Implementation of an Internet of Things (IoT)-Based Water Distribution and Management System. Clean. Technol. Environ. Policy 2021, 23, 271–283. [Google Scholar] [CrossRef]
- Feliciano, J.F.; Arsénio, A.M.; Cassidy, J.; Santos, A.R.; Ganhão, A. Knowledge Management and Operational Capacity in Water Utilities, a Balance between Human Resources and Digital Maturity—The Case of AGS. Water 2021, 13, 3159. [Google Scholar] [CrossRef]
- Feliciano, J.; Almeida, R.; Santos, A.; Ramalho, P.; Ganhão, A.; Covas, D.; Alegre, H. Assessing Human Resources Renovation Needs in Water Utilities. Water Pract. Technol. 2016, 11, 728–735. [Google Scholar] [CrossRef]
- Chirica, Ş.; Luca, M.; Lateş, I. Updating the pipe networks layout plan using modern detection equipment. Hidrotehnica 2018, 64, 17–25. [Google Scholar]
- Sridharan, N.; Pandey, R.U.; Berger, T. Co-Production through Tacit Knowledge for Water Resilience. Land. Use Policy 2023, 126, 106446. [Google Scholar] [CrossRef]
- Areiza, J.; Caraballo, J. Análisis de Las Pérdidas de Agua En Los Sistemas de Abastecimiento Público, Identificando Sectores y Causas Influyentes En Los Altos Índices de Agua No Contabilizada (IANC) En El Municipio de Turbo Antioquia—10596/28359; Universidad Nacional Abierta y a Distancia UNAD: Bogotá, Colombia, 2019. [Google Scholar]
- Rossman, L.A.; Woo, H.; Tryby, M.; Shang, F.; Janke, R. Manual Del Usuario de EPANET 2.2; EPA, US Environmental Protection Agency: Washington, DC, USA, 2002. [Google Scholar]
- Avesani, D.; Righetti, M.; Righetti, D.; Bertola, P. The Extension of EPANET Source Code to Simulate Unsteady Flow in Water Distribution Networks with Variable Head Tanks. J. Hydroinform. 2012, 14, 960–973. [Google Scholar] [CrossRef]
- Todini, E.; Pilati, S. A Gradient Method for the Solution of Looped Pipe Networks. In Computer Applications in Water Supply; Coulbeck, B., Orr, C.H., Eds.; systems analysis and simulation; John Wiley & Sons: London, UK, 1988; Volume 1, pp. 1–20. [Google Scholar]
- Ingeniousware Software for Smarter Water Network Monitoring—WATERing Online. Available online: https://watering.online/watering/ (accessed on 1 February 2023).
- Klingel, P. Technical Causes and Impacts of Intermittent Water Distribution. Water Supply 2012, 12, 504–512. [Google Scholar] [CrossRef]
- Kaamin, M.; Azhar, S.; Tajudin, A.; Athirah Basri, N.; Rahman, R.A.; Hakimi Mat Nor, A.; Azraie, M.; Kadir, A.; Mokhtar, M.; Luo, P. Unmanned Aerial Vehicle Technology Use in Visual Road Inspection at Ft005, Johor Bahru-Melaka. Int. J. Nanoelectron. Mater. 2022, 15, 37–48. [Google Scholar]
- Agisoft LLC Agisoft Metashape User Manual Professional Edition, Version 1.7; Agisoft: St. Petersburg, Russia, 2021.
- Soriano Olivares, J. Cómo Convertir una Red de Tubería en Autocad a una Red en Epanet. Available online: http://hdl.handle.net/10251/82990 (accessed on 4 February 2023).
- Tapiero, D.I.S.; Valencia, M.M. SIG Aplicado a La Optimización Del Tiempo de Diseño En Redes de Distribución de Agua Potable. Ing. Hidráulica Ambient. 2021, 42, 68–80. [Google Scholar]
- Vegas, O.T.; Santos, R.; Delgado, X.; Fernando, M.A.; Rodriguez, J. Herramienta para convertir un modelo de datos de una red de suministro de agua en formato shape y epanet. In Proceedings of the 5tas Jornadas México—11vas Jornadas Latinoamericanas y del Caribe de gvSIG, Guanajuato, Mexico, 15–16 August 2019; pp. 1–9. [Google Scholar]
- Ociepa, E.; Mrowiec, M.; Deska, I. Analysis of Water Losses and Assessment of Initiatives Aimed at Their Reduction in Selected Water Supply Systems. Water 2019, 11, 1037. [Google Scholar] [CrossRef]
- Alcaldía de Pamplona. Plan Básico de Ordenamiento Territorial Municipio de Pamplona Norte de Santander 2015. 2015. Available online: https://pamplona-nortedesantander.gov.co/Transparencia/PlaneacionGestionyControl (accessed on 15 February 2023).
- Bonilla Granados, C.A.; Tarazona Tobo, L.V.; Caicedo Calderón, A.D. Statistical Analysis of Residential Drinking Water Consumption in Toledo, Colombia. BISTUA Rev. Fac. Cienc. Básicas 2022, 20, 70–75. [Google Scholar] [CrossRef]
- Herrera, D.B.; Ávila, E.M.; Mejía, C.A.Z. Analysis of non-revenue water in the urban supply system of the municipality of Facatativá, Colombia. Tecnura 2020, 24, 84–98. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bonilla, C.; Brentan, B.; Montalvo, I.; Ayala-Cabrera, D.; Izquierdo, J. Digitalization of Water Distribution Systems in Small Cities, a Tool for Verification and Hydraulic Analysis: A Case Study of Pamplona, Colombia. Water 2023, 15, 3824. https://doi.org/10.3390/w15213824
Bonilla C, Brentan B, Montalvo I, Ayala-Cabrera D, Izquierdo J. Digitalization of Water Distribution Systems in Small Cities, a Tool for Verification and Hydraulic Analysis: A Case Study of Pamplona, Colombia. Water. 2023; 15(21):3824. https://doi.org/10.3390/w15213824
Chicago/Turabian StyleBonilla, Carlos, Bruno Brentan, Idel Montalvo, David Ayala-Cabrera, and Joaquín Izquierdo. 2023. "Digitalization of Water Distribution Systems in Small Cities, a Tool for Verification and Hydraulic Analysis: A Case Study of Pamplona, Colombia" Water 15, no. 21: 3824. https://doi.org/10.3390/w15213824