International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Digital Twin based Household Water Consumption Forecasting using Agent Based Modeling

  • Sultan Alamri (Department of Computer Science, Saudi Electronic University) ;
  • Muhammad Saad Qaisar Alvi (Department of Computer Science, The Islamia University of Bahawalpur) ;
  • Imran Usman (Department of Computer Science, National University of Sciences and Technology Balochistan Campus) ;
  • Adnan Idris (Department of Computer Science, National University of Sciences and Technology Balochistan Campus)
  • Received : 2024.04.05
  • Published : 2024.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

Keywords

References

  1. Y.B. Moon, "Simulation Modeling for Sustainability: A Review of the Literature," International Journal of Sustainable Engineering, vol. 10, no. 1, pp. 2-19, 2017  https://doi.org/10.1080/19397038.2016.1220990
  2. X. Nie, X. Zou1and D.Zhu "Modeling and Simulation of Entrepreneur Individual Based on Dynamic and Complex System Computing," Computer Systems Science and Engineering, Vol.34, No.4, pp. 207-214, 2019, DOI:10.32604/csse.2019.34.207 
  3. M. E. Bayrakdar,"Cost Effective Smart System for Water Pollution Control with Underwater Wireless Sensor Networks: A Simulation Study," Computer Systems Science and Engineering, Vol.35, No.4, pp. 283-292, 2020, DOI:10.32604/csse.2020.35.283 
  4. D. Butler, S. Ward, C. Sweetapple, M. Astaraie-Imani, K. Diao, et al. "Reliable, resilient and sustainable water management: the Safe & Sure approach," Global Challenges, vol.1, no.1, pp.63- 77. doi: 10.1002/gch2.1010. 
  5. Anylogic. Retrieved From Anylogic: Simulation Modeling Software Tools & Solutions. 2018 [Online] Available: https://www.Anylogic.com/ 
  6. H. Ji, J. Li, S. Zhang and Q. Wu, "Research on Water Resources Intelligent Management of Thermal Power Plant Based on Digital Twins," in Proc. IEEE, Chengdu, China, pp. 557-562, 2021, doi: 10.1109/ICCCBDA51879.2021.9442503. 
  7. A. Azoulay, "Nature Based Solution for Water," United Nations Water Report, Brasilia, pp. 1-4, 2018. 
  8. R. Zaher and G. Badr, "Intelligent Software Simulation of Water Consumption in Domestic Homes," in Proc. UKSim-AMSS, Cambridge, UK, pp. 99-104, 2016. doi: 10.1109/UKSim.2016.30. 
  9. Y. Yannou, Leroy, T. Zaraket and E. Chapotot, "Activity based simulation of households energy and water consumptions," Hal Archives, pp. 1-9, 2017. https://hal.archives-ouvertes.fr/hal-01448717 
  10. L. Linkola, C. J. Andrews and T. Schuetze, "An Agent Based Model of Household Water Use," Water, vol. 5, no.3, pp. 1082-1100, 2013, https://doi.org/10.3390/w5031082 
  11. E. Bonabeau, "Agent-based modeling: Methods and techniques for sim- ulating human systems.," in Proc. Proc Natl Acad Sci U S A, pp. 7280-7287, 2002.http://doi:10.1073/pnas.082080899 
  12. U. Wilensky and W. Rand, "An Introduction to Agent-Based Modeling", b & w illus., 2015, [Online]. Available: https://mitpress.mit.edu/9780262328135/an-introduction-to-agent-based-modeling/ 
  13. P. Williamson, "An applied microsimulation model: Exploring alternative domestic water consumption scenarios," In Regional science in business, pp. 243-268, 2001. 
  14. Z. Berglund,"Using Agent-Based Modeling for Water Resources Planning and Management," Journal of Water Resources Planning and Management, vol. 141, no.11, pp. 89-97, 2015.  https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544
  15. M. Alvi, I. Mahmood, F. Javed, A. Malik, and H. Sarjoughian, "Dynamic Behavioral Modeling, Simulation and Analysis of Household Water Con- sumption in an Urban Area: A Hybrid Approach, " in Proc. WSC, Gothenburg, Sweden, pp. 2411-2422, 2018. 
  16. P. Darbandsari, and R. Kerachian, "An Agent-based behavioral simulation model for residential water demand management: The case-study of Tehran, Iran," Simulation Modeling Practice and Theory, vol. 78, pp. 51- 72, 2017.  https://doi.org/10.1016/j.simpat.2017.08.006
  17. I. Koutiva and C. Makropoulos,"Modelling domestic water demand: An agent based approach," Environmental Modeling and Software, vol. 79, pp. 35-54, 2016.  https://doi.org/10.1016/j.envsoft.2016.01.005
  18. J. M. Galan, A. L'opez Paredes and R. Olmo, "An agent-based model for domestic water management in Valladolid metropolitan area," Water Resources Research, vol. 45, pp. 61-72, 2009. https:// doi:10.1029/2007WR006536 
  19. C. A. Wanderbeg, K. Patricia, O. Esquerre, and Oz Sahin., "Building a system dynamics model to support water management: A case study of the semiarid region in the Brazilian Northeast," Water, vol.11, no.12, 2019, https://doi.org/10.3390/w11122513. 
  20. P. C. Fuertes, F. M. Alzamora , M. H. Carot and J.C. A. Campos,"Building and exploiting a Digital Twin for the management of drinking water distribution networks," Urban Water Journal, vol.17, no.8, pp. 704-713, 2020.  https://doi.org/10.1080/1573062X.2020.1771382
  21. M. J. Kaur, V. P. Mishra and P. Maheshwari, "The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action," Digital Twin Technologies and Smart Citie , Springer, 2020. 
  22. R. G. Alves, G. Souza, R.F Maia, A.L.Ho Tran, C. Kamienski et al., "A digital twin for smart farming," in Proc IEEE, Seattle, WA, USA, pp. 2377-6919, 2019, https://10.1109/GHTC46095.2019.9033075 
  23. A. Anagnostou and S.J.E. Taylor, "A distributed simulation methodological framework for OR/MS applications," Simulation Modelling Practice and Theory, vol. 70, pp. 101-119, 2017 https://doi.org/10.1016/j.simpat.2016.10.007