Skip to main content
SpringerLink
Log in

Digital Twin for Regional Water Consumption Simulation and Forecasting

  • Conference paper
  • First Online:
Optimization, Learning Algorithms and Applications (OL2A 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1981))

  • 184 Accesses

Abstract

Water scarcity is a global concern due to population growth, climate change, and industrialization. Accurate water consumption simulation and forecasting are essential for understanding consumption patterns and predicting future demand. The control and visualization of how different aspects such as precipitation, season, and population affect water consumption can be a way for public agencies to plan actions that minimize waste and assist in the correct use of water. Technology, and especially Machine Learning and Digital Twin, can be used as tools for this. In light of this, this project aims to develop a system for simulating and forecasting water consumption in the Bragança region using a Digital Twin. In order to accomplish this, a comprehensive analysis is conducted to determine the necessary requirements for designing the system. This analysis encompasses the evaluation of hardware, software, data, machine learning models, web interface, as well as security and performance requirements. Furthermore, the architecture of this system and how it will be configured is analyzed, proposing a system with Training Data Sources, Training Process, Updated Data Sources, Digital Twin, Web Interface and Monitoring System. The system described in this article is under development and it is hoped to achieve as a result the full design of the Digital Twin and User Interface systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alves, R.G., Maia, R.F., Lima, F.: Development of a digital twin for smart farming: irrigation management system for water saving. J. Clean. Prod. 388, 135920 (2023)

    Article  Google Scholar 

  2. Botín-Sanabria, D.M., Mihaita, A.S., Peimbert-García, R.E., Ramírez-Moreno, M.A., Ramírez-Mendoza, R.A., Lozoya-Santos, J.D.J.: Digital twin technology challenges and applications: a comprehensive review. Remote Sens. 14(6), 1335 (2022)

    Google Scholar 

  3. Cao, L., Yuan, X., Tian, F., Xu, H., Su, Z.: Forecasting of water consumption by integrating spatial and temporal characteristics of short-term water use in cities. Phys. Chem. Earth Parts A/B/C 130, 103390 (2023). https://doi.org/10.1016/j.pce.2023.103390. https://www.sciencedirect.com/science/article/pii/S1474706523000347

  4. Cesco, S., Sambo, P., Borin, M., Basso, B., Orzes, G., Mazzetto, F.: Smart agriculture and digital twins: applications and challenges in a vision of sustainability. Eur. J. Agron. 146, 126809 (2023)

    Article  Google Scholar 

  5. Fonseca, Í.A., Gaspar, H.M., de Mello, P.C., Sasaki, H.A.U.: A standards-based digital twin of an experiment with a scale model ship. Comput. Aided Des. 145, 103191 (2022)

    Article  Google Scholar 

  6. Fuller, A., Fan, Z., Day, C., Barlow, C.: Digital twin: enabling technologies, challenges and open research. IEEE Access 8, 108952–108971 (2020)

    Article  Google Scholar 

  7. Gubareva, R.: Extracting temporal patterns from smart city data. Master’s thesis (2022). https://bibliotecadigital.ipb.pt/handle/10198/25870. Accepted 02 Sept 2022

  8. Kavya, M., Mathew, A., Shekar, P.R., P, S.: Short term water demand forecast modelling using artificial intelligence for smart water management. Sustain. Cities Soc. 95, 104610 (2023). https://doi.org/10.1016/j.scs.2023.104610. https://www.sciencedirect.com/science/article/pii/S2210670723002214

  9. Liu, X., et al.: A systematic review of digital twin about physical entities, virtual models, twin data, and applications. Adv. Eng. Inform. 55, 101876 (2023)

    Article  Google Scholar 

  10. Lu, Q., et al.: Developing a digital twin at building and city levels: case study of west Cambridge campus. J. Manag. Eng. 36(3), 05020004 (2020)

    Article  Google Scholar 

  11. World Health Organization: State of the world’s drinking water: an urgent call to action to accelerate progress on ensuring safe drinking water for all (2022)

    Google Scholar 

  12. Pesantez, J.E., Alghamdi, F., Sabu, S., Mahinthakumar, G., Berglund, E.Z.: Using a digital twin to explore water infrastructure impacts during the covid-19 pandemic. Sustain. Urban Areas 77, 103520 (2022)

    Google Scholar 

  13. Tao, F., Xiao, B., Qi, Q., Cheng, J., Ji, P.: Digital twin modeling. J. Manuf. Syst. 64, 372–389 (2022)

    Article  Google Scholar 

  14. Wolf, K., Dawson, R.J., Mills, J.P., Blythe, P., Morley, J.: Towards a digital twin for supporting multi-agency incident management in a smart city. Sci. Rep. 12(1), 16221 (2022)

    Article  Google Scholar 

  15. Yeon, H., Eom, T., Jang, K., Yeo, J.: Dtumos, digital twin for large-scale urban mobility operating system. Sci. Rep. 13(1) (2023). https://doi.org/10.1038/s41598-023-32326-9. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151176448 &doi=10.1038%2fs41598-023-32326-9 &partnerID=40 &md5=a596bf448f95164776c3ee18d1f2182a. All Open Access, Gold Open Access, Green Open Access

  16. Zekri, S., Jabeur, N., Gharrad, H.: Smart water management using intelligent digital twins. Comput. Inform. 41(1), 135–153 (2022)

    Article  Google Scholar 

  17. Zhang, X., et al.: Real-time pipeline leak detection and localization using an attention-based LSTM approach. Process Saf. Environ. Prot. 174, 460–472 (2023). https://doi.org/10.1016/j.psep.2023.04.020. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152937802 &doi=10.1016%2fj.psep.2023.04.020 &partnerID=40 &md5=3ab0978b41f5b91613e529429b1c7bdd

Download references

Acknowledgments

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/AI/0088/2020.

Author information

Authors and Affiliations

  1. Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

    Matheus Galvão, Pedro Rici & Rui Pedro Lopes

  2. Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Bragança, Portugal

    Matheus Galvão, Pedro Rici & Rui Pedro Lopes

Authors
  1. Matheus Galvão
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Rici
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rui Pedro Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rui Pedro Lopes .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Azores, Ponta Delgada, Portugal

    Armando Mendes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Galvão, M., Rici, P., Lopes, R.P. (2024). Digital Twin for Regional Water Consumption Simulation and Forecasting. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53025-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53024-1

  • Online ISBN: 978-3-031-53025-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation