Advertisement

Disaster Prevention Virtual Advisors Through Soft Sensor Paradigm

  • Agnese AugelloEmail author
  • Umberto Maniscalco
  • Giovanni Pilato
  • Filippo Vella
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 55)

Abstract

In this paper we illustrate the architecture of an intelligent advisor agent aimed at limiting, or as far as possible preventing, the damages caused by catastrophic events, such as floods and landslides. The agent models the domain and makes forecasting by exploiting both ontology models and belief network models. Furthermore, it uses a monitoring network to recommend preventive measures and giving alerts, if necessary, before that the event happens. The monitoring network can be implemented through both physical and soft sensors: this choice makes the measurements more adequate and available also in case of failure of some of the physical sensors. The front-end of the agent is made by a chat-bot, capable to interact with human users using natural language.

Keywords

Decision support systems Intelligent conversational agents Soft sensors 

Notes

Acknowledgments

We would like to thank Emanuele Cipolla and Dario Stabile for their work in the set up of the visualization system and the collection of the data inside the activities for the systems able to filter data and process information for environmental multi risk analysis.

References

  1. 1.
    Akyildiz, I.F., et al.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Augello, A., Pilato, G., Gaglio, S.: Intelligent advisor agents in distributed environments. In: Information Retrieval and Mining in Distributed Environments, pp. 109–124. Springer, Berlin (2010)Google Scholar
  3. 3.
    Augello, A., Pilato, G., Vassallo, G., Gaglio, S.: Chatbots as interface to ontologies. In: Advances onto the Internet of Things, pp. 285–299. Springer (2014)Google Scholar
  4. 4.
    Babitski, G., Probst, F., Hoffmann, J., Oberle, D.: Ontology design for information integration in disaster management. GI Jahrestagung 154, 3120–3134 (2009)Google Scholar
  5. 5.
    Chong, C.-Y., Srikanta, P.: Sensor networks: evolution, opportunities, and challenges. Proc. IEEE 91(8), 1247–1256 (2003)CrossRefGoogle Scholar
  6. 6.
    Ciarlini, P., Maniscalco, U., Regoliosi, G.: Validation of soft sensors in monitoring ambient parameters. In: Advanced Mathematical and Computational Tools in Metrology and Testing VII, vol. 72, p. 142 (2006)Google Scholar
  7. 7.
    Cipolla, E., Maniscalco, U., Rizzo, R., Stabile, D., Vella, F.: Analysis and visualization of meteorological emergencies. J. Ambient Intell. Hum. Comput. (2016)Google Scholar
  8. 8.
    Cipolla, E., Vella, F.: Boosting of association rules for robust emergency detection. In: 2015 Eleventh International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 248–255. IEEE (2015)Google Scholar
  9. 9.
    Cipolla, E., Vella, F.: Identification of spatio-temporal outliers through minimum spanning tree. In: 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 248–255. IEEE (2014)Google Scholar
  10. 10.
    Giretti, A., Carbonari, A., Naticchia, B.: A spatio-temporal Bayesian network for adaptive risk management in territorial emergency response operations. INTECH Open Access Publisher (2012)Google Scholar
  11. 11.
    Kim, K.-M., Hong, J.-H., Cho, S.-B.: A semantic Bayesian network approach to retrieving information with intelligent conversational agents. Inf. Process. Manag. 43 (2007)Google Scholar
  12. 12.
    Maniscalco, U., Pilato, G., Vassallo, G.: Soft Sensor based on E-\(\alpha \)NETs. In: Apolloni, B., Bassis, S., Morabito, C.F. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 226, pp. 172–179 (2010). ISSN: 0922-6389Google Scholar
  13. 13.
    Maniscalco, U., Rizzo, R.: A virtual layer of measure based on soft sensors. J. Ambient Intell. Hum. Comput. pp. 1–10 (2016)Google Scholar
  14. 14.
    Maniscalco, U., Rizzo, R.: Adding a virtual layer in a sensor network to improve measurement reliability. In: Advanced Mathematical and Computational Tools in Metrology and Testing X. World Scientific Publishing Co., Singapore, pp. 260–264 (2015)Google Scholar
  15. 15.
    Maniscalco, U.: Virtual sensors to support the monitoring of cultural heritage damage. In: Biological and Artificial Intelligence Environments, pp. 343–350 (2005)Google Scholar
  16. 16.
    Maniscalco, U., Pilato, G.: Multi soft-sensors data fusion in spatial forecasting of environmental parameters. Adv. Math. Comput. Tools Metrol. Test. IX 84, 252–259 (2012)CrossRefGoogle Scholar
  17. 17.
    Molina, M., Fuentetaja, R., Garrote, L.: Hydrologic models for emergency decision support using Bayesian networks. In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Springer, Berlin, pp. 88–99 (2005)Google Scholar
  18. 18.
    Nielsen, T.D., Jensen, F.V.: Bayesian Networks and Decision Graphs. Information Science and Statistics, 2nd ed. vol. XVI, 448 p. (2007). ISBN: 978-0-387-68281-5Google Scholar
  19. 19.
    Power, D.J., Sharda, R., Burstein, F.: Decision Support Systems. Wiley (2015)Google Scholar
  20. 20.
    Song, L., Jie, W., Hui, Y., He-ping, Z.: Bayesian network model for fast disaster assessment in unconventional emergencies management. In: 2011 International Conference on Information Systems for Crisis Response and Management (ISCRAM), pp. 375–381. IEEE (2011)Google Scholar
  21. 21.
    Symeonidis, A.L., Kyriakos, C.C., Athanasiadis, I.N., Mitkas, P.A.: Data mining for agent reasoning: a synergy for training intelligent agents. Eng. Appl. Artif. Intell. 20(8), pp. 1097–1111 (2007). doi: 10.1016/j.engappai.2007.02.009. ISSN: 0952-1976Google Scholar
  22. 22.
    Williamson, J.: Bayesian Nets and Causality: Philosophical and Computational Foundations (2004)Google Scholar
  23. 23.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Agnese Augello
    • 1
    Email author
  • Umberto Maniscalco
    • 1
  • Giovanni Pilato
    • 1
  • Filippo Vella
    • 1
  1. 1.Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR (CNR)PalermoItaly

Personalised recommendations