Skip to main content

Water Distribution Network Perspective in RAFAEL Project, A System for Critical Infrastructure Risk Analysis and Forecast

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Abstract

RAFAEL is the acronym for “system for Risk Analysis and Forecast for critical infrastructure in ApenninEs dorsaL regions” project (PNR 2015–2020 Italian Ministry of University and Research). As part of technological developments undertaken over the last few years, it aims at integrating ad hoc technologies, developed within the project, into a platform, the CIPCast Decision Support System (DSS), which will become the reference platform to support the critical infrastructures (CI) protection and risk analysis, in favour of the Operators and the Public Administration. RAFAEL deals with the management of numerous CI evaluating the damages of natural disastrous on individual elements. the impacts on the services and the consequences on the interdependent CIs. The water supply network issue is approached in the presented research, by means of a heuristic approach. The relevant impacts on the water distribution system have been investigated through the combination of a hydraulic simulation model and a reliability analysis of the hydraulic parameters. The methodology has been applied to the Castel San Giorgio water distribution network.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Baloye, D.O., Palamuleni, L.G.: Urban critical infrastructure interdependencies in emergency management: findings from Abeokuta Nigeria. Disaster Prev. Manage. 26(2), 162–182 (2017)

    Article  Google Scholar 

  2. Griot, C.: Modelling and simulation for critical infrastructure interdependency assessment: a meta-review for model characterization. Int. J. Crit. Infrastruct. 6(4), 363–379 (2010)

    Article  Google Scholar 

  3. Chou, C.-C., Tseng, S.-M.: Collection and analysis of critical infrastructure interdependency relationships. J. Comput. Civ. Eng. 24(6), 539–547 (2010)

    Article  Google Scholar 

  4. Moriconi, C., Pollino, M., Rosato, V.: La protezione delle Infrastrutture Critiche e il controllo del territorio. Energia ambiente e innovazione 1, 52–57 (2017)

    Google Scholar 

  5. Luiijf, E., et al.: Empirical findings on critical infrastructure dependencies in Europe. 5508(2009), 302-310 (2009)

    Google Scholar 

  6. Varianou Mikellidou, C., Shakou, L.M., Boustras, G., Dimopoulos, C.: Energy critical infrastructures at risk from climate change: a state of the art review. Safety Sci. 110, 110–120 (2018)

    Article  Google Scholar 

  7. Rosato, V., Di Pietro, A., La Porta, L., Pollino, M., Tofani, A., Marti, J.R., Romani, C.: A decision support system for emergency management of critical infrastructures subjected to natural hazards. In: Panayiotou, C.G.G., Ellinas, G., Kyriakides, E., Polycarpou, M.M.M. (eds.) Critical Information Infrastructures Security. LNCS, vol. 8985, pp. 362–367. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31664-2_37

    Chapter  Google Scholar 

  8. Omidvar, B., Hojjati Malekshah, M., Omidvar, H.: Failure risk assessment of interdependent infrastructures against earthquake, a Petri net approach: case study-power and water distribution networks. Nat. Hazards 71(3), 1971–1993 (2014)

    Article  Google Scholar 

  9. Fattoruso, G., et al.: Modeling electric and water distribution systems interdependences in urban areas risk analysis. In: COWM 2nd International Conference Citizen Observatories for Natural Hazards and Water Management, Venice, pp. 222–225 (2018)

    Google Scholar 

  10. RAFAEL. https://www.progetto-rafael.it/. Accessed 12 May 2021

  11. Di Pietro, A., Lavalle, L., La Porta, L., Pollino, M., Tofani, A., Rosato, V.: Design of DSS for supporting preparedness to and management of anomalous situations in complex scenarios. In: Setola, R., Rosato, V., Kyriakides, E., Rome, E. (eds.) Managing the Complexity of Critical Infrastructures. SSDC, vol. 90, pp. 195–232. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-51043-9_9

    Chapter  Google Scholar 

  12. Taraglio, S.: A Lombardi: decision support system for smart urban management: resilience against natural phenomena and aerial environmental assessment. Int. J. Sustain. Energy Plann. Manage. 24 (2019)

    Google Scholar 

  13. Matassoni, L., Giovinazzi, S., Pollino, M., Fiaschi, A., La Porta, L., Rosato, V.: A geospatial decision support tool for seismic risk management: florence (Italy) case study. In: Gervasi, O. (ed.) Computational Science and Its Applications – ICCSA 2017. LNCS, vol. 10405, pp. 278–293. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62395-5_20

    Chapter  Google Scholar 

  14. Gervasi, O. (ed.): Computational Science and Its Applications – ICCSA 2017. LNCS, vol. 10404. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62392-4

    Book  Google Scholar 

  15. Modica, G., et al.: Land suitability evaluation for agro-forestry: definition of a web-based multi-criteria spatial decision support system (MC-SDSS): preliminary results. In: Gervasi, O. (ed.) Computational Science and Its Applications -- ICCSA 2016. LNCS, vol. 9788, pp. 399–413. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42111-7_31

    Chapter  Google Scholar 

  16. Ottobrino, V., Esposito, T., Locoratolo, S.: Towards a smart water distribution network for assessing the effects by critical situations in electric networks. the pilot case of Castel San Giorgio. In: 21th International Conference on Computational Science and its Application, RRS2021 Workshop, Cagliari, Italy (2021)

    Google Scholar 

  17. Agresta, A., et al.: Applying numerical models and optimized sensor networks for drinking water quality control. Proc. Eng. 119, 918–926 (2015). ISSN 1877-7058

    Google Scholar 

  18. Fattoruso, G., Agresta, A., Guarnieri, G., Toscanesi, M., De Vito, S., Fabbricino, M., Trifuoggi, M., Di Francia, G.: A software system for predicting trihalomethanes species in water distribution networks using online networked water sensors. In: Di Francia, G. (ed.) Sensors and Microsystems. LNEE, vol. 629, pp. 417–423. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37558-4_62

    Chapter  Google Scholar 

  19. Behzadian, K., Kapelan, Z., Savic, D., Ardeshir, A.: Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environ. Model. Softw. 24(4), 530–541 (2009)

    Article  Google Scholar 

  20. Fattoruso, G., et al.: Optimal sensors placement for flood forecasting modelling. Proc. Eng. 119, 927–936 (2015). ISSN 1877-7058

    Google Scholar 

  21. Gervasi, O. (ed.): Computational Science and Its Applications – ICCSA 2020. LNCS, vol. 12251. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58808-3

    Book  Google Scholar 

Download references

Acknowledgments

The research activities described in the present paper have been carried out in the framework of the RAFAEL project, co-funded by Italian Ministry of University and Research, MUR, Grant no. ARS01_00305.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Antonia Longobardi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Longobardi, A. et al. (2021). Water Distribution Network Perspective in RAFAEL Project, A System for Critical Infrastructure Risk Analysis and Forecast. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12956. Springer, Cham. https://doi.org/10.1007/978-3-030-87010-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87010-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87009-6

  • Online ISBN: 978-3-030-87010-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics