Skip to main content

Advertisement

Log in

Ontology-driven slope modeling for disaster management service

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

These days, with the development of information technology, new paradigms have been created through academical and technological convergence in various areas. The IT convergence draws much attention as the next generation technology for disaster prevention and management in the construction and transportation area. Along with global warming, global climate changes and unusual weather occur around the world, and consequently disasters become more huge. IT convergence based disaster management service makes it possible to quickly respond to unexpected disasters in the ubiquitous environment and mitigate the disasters. Although research on disaster prevention and management has constantly been conducted, it is relatively slow to develop the technology for disaster prediction and prevention. For efficient safety and disaster prevention and management in the next generation IT convergence, it is essential to establish a systematic disaster prevention technology and a disaster prevention information system. In this paper, we proposed ontology-driven slope modeling for disaster management service through the convergence of construction, transportation technology and IT. User profile, environment information, location information, weather index, slope stability, disaster, statistics and analysis of disasters, and forest fire disaster index are used to build internal context information, external context information, and service context information. Ontology-based context awareness modeling of the landslides and disasters generated is constructed, and relevant rules are generated by inference engine. Based on the ontology of external and internal context awareness, the rules of service inference derived by inference engine are produced using protégé 5.0. According to the service inference rules, disaster control services best fitting for users’ environment is provided. By addressing the social issues related to disaster prevention and response and judging the potential risk of disasters, the proposed method can contribute to improving the safety of the public and the quality of their life. Social consensus on the necessity of prevention of urban climate disasters can be formed easily, and a ripple effect is expected on the situational response to natural disaster.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Korea Meteorological Administration, http://web.kma.go.kr/.

  2. National Disaster Information Center, http://www.safekorea.go.kr/.

  3. Korea Forest Service, http://www.forest.go.kr/.

  4. Ontology Web Language, http://www.w3.org/TR/owl-features/.

  5. protégé, http://protege.stanford.edu/.

  6. Jena, http://jena.apache.org/.

    Fig. 10
    figure 10

    Classes and relations in the integrated hierarchical disaster management ontology

References

  1. Clcoksin, W.F.: Artificial intelligence and the future philosophical transactions. Math. Phys. Eng. Sci. 361(3), 1721–1748 (2003)

    Article  Google Scholar 

  2. Jung, H., Yoo, H., Chung, K., Lee, Y.: Performance analysis of intelligence pain nursing intervention U-health system. J. Korea Contents Assoc. 13(4), 1–7 (2013)

    Article  Google Scholar 

  3. Jung, H., Chung, K.: Discovery of automotive design paradigm using relevance feedback. Pers. Ubiquitous Comput. 18(6), 1363–1372 (2014)

    Article  Google Scholar 

  4. Chung, K.Y.: Recent trends on convergence and ubiquitous computing. Pers. Ubiquitous Comput. 18(6), 1291–1293 (2014)

    Article  Google Scholar 

  5. Song, G.Y., Cheon, Y., Lee, K., Lim, H., Chung, K.Y., Rim, H.C.: Multiple categorizations of products: cognitive modeling of customers through social media data mining. Pers. Ubiquitous Comput. 18(6), 1387–1403 (2014)

    Article  Google Scholar 

  6. Chun, H.W.: Disaster prevention information technology. Electron. Telecommun. Trends, 140, 145–154 (2013)

  7. Ryu, S.-I., Ahn, H.-W.: Improving disaster response system using network—focused on Korea and Japan’s disaster cases. J. Korea Contents Assoc. 7(2), 170–179 (2007)

  8. Barry, A., Nick, F.: Man-Made Disasters. Butterworth-Heinemann, Oxford (1997)

  9. Chae, K.S.: A comparative study on the disaster management system in local government. J. Local Gov. Stud. 8(4), 129–145 (2005)

    Google Scholar 

  10. Seo, T.W., Jung, D.H., Jeong, M.G., Kim, C.S.: Design of cyber disaster management system using IT conversions technology. Korean Soc Internet Inf. 6, 811–815 (2010)

    Google Scholar 

  11. Department of Homeland Security, US. http://www.dhs.gov

  12. Ministry of Government Administration and Home Affairs, Korea. http://www.mospa.go.kr

  13. Central Disaster and Safety Countermeasures Headquarters. http://www.snskorea.go.kr

  14. Kang, S.K., Chung, K.Y., Ryu, J.K., Rim, K.W., Lee, J.H.: Bio-interactive healthcare service system using lifelog based context computing. Wirel. Pers. Commun. 73(2), 341–351 (2013)

    Article  Google Scholar 

  15. Kang, S.K., Chung, K.Y., Lee, J.H.: Real-time tracking and recognition systems for interactive telemedicine health services. Wirel. Pers. Commun. 79(4), 2611–2626 (2014)

    Article  Google Scholar 

  16. Wang, B.R., Park, J.Y., Chung, K.Y., Choi, I.: Influential factors of smart health users according to usage experience and intention to use. Wirel. Pers. Commun. 79(4), 2671–2683 (2014)

    Article  Google Scholar 

  17. Oh, S.Y., Ghose, S., Chung, K., Han, J.S.: Recent trends in convergence based smart healthcare service. Int J. Technol. Health Care 22(3), 303–307 (2014)

    Google Scholar 

  18. Jung, E.Y., Kim, J., Chung, K.Y., Park, D.K.: Mobile healthcare application with EMR interoperability for diabetes patients. Cluster Comput. 17(3), 871–880 (2014)

    Article  Google Scholar 

  19. Chung, K., Boutaba, R., Hariri, S.: Recent trends in digital convergence information system. Wirel. Pers. Commun. 79(4), 2409–2413 (2014)

    Article  Google Scholar 

  20. Oh, S.Y., Ghose, S., Jang, H.J., Chung, K.: Recent trends in mobile communication systems. Int. J. Comput. Virol. hacking 10(2), 67–70 (2014)

    Article  Google Scholar 

  21. Federal Emergency Management Agency. http://www.fema.gov

  22. Cabinet Office Disaster Management, Japan. http://www.bousai.go.jp

  23. Lee, S.H.: International disaster safety wireless network deployment and operational trends. Wkly. Tech. Trends 1520, 5 (2011)

    Google Scholar 

  24. Chang, S.C.: A synchronous cooperative communication for emergency alert broadcast based on cellular systems. J. Broadcast Eng. 19(2), 184–194 (2014)

  25. Skempton, A.W., Hutchinson, J.H.: Stability of natural slopes and embankment foundations. State-of-the art report, Proc. of 7th Int., Conf. SMFE, Mexico. City 2, 291–335 (1948)

  26. Petterson, K.E.: The early history of circular sliding surfaces. J. Géotech. 5, 275–296 (1955)

    Article  Google Scholar 

  27. Fellenius, W.: Calculation of the stability of earth dams. Second congress on large dams, pp. 445\(\sim \)462, 1986

  28. A. W. Bishop, L. Bjerrum, “The Relevance of the Triaxial Test to the Solution of Stability Problems” Proc., ASCE Reserach Conf. on Shear Strength of Cohesive Soils, Boulder, Col., pp. 437–501. 1960

  29. Janbu, N.: Slope Stability Computations in Embankment Dam Engineering, Casagrande Memorial Volume, pp. 47–86. Wiley, New York (1973)

    Google Scholar 

  30. Nash, D., Anderson, M.G., Richards, K.S.: Slope Stability: A Comparative Review of Limit Equilibrium Methods of Stability Analysis, pp. 11–75. Wiley, New York (1987)

    Google Scholar 

  31. Kim, S.H.: Development of the 3D viewer of slope stability analysis for Rockfall and Landslide prevention system, Final Report, Korea Agency for Infrastructure Technology Advancement (2008)

  32. Schilit, B.N., Adams, N., Want, R.: Context-aware computing applications, In Proc. of the workshop on mobile computing system and applications, pp. 85–90, (1994)

  33. Merriam-Webster Inc, : Merriam Webster’s Collegiate Dictionary. Merriam-Webster, Springfield (1997)

    Google Scholar 

  34. Brown, P.J., Bovey, J.D., Chem, X.: Context-aware applications: from the laboratory to the marketplace. IEEE Pers. Commun. 4, 58–64 (1997)

  35. Dey, A.K.: Providing architectural support for building context-aware applications. Ph.D. thesis, Georgia Institute of Technology, (2000)

  36. Gu, T., Pung, T.H. K., Zhang, D.Q.: An ontology-based context model in intelligent environments. In Proc. of communication networks and distributed systems modeling and simulation conference, 2004: 270–275 (2004)

  37. Kim, J.H.: Context-information based item recommendation technique for personalized U-healthcare service. Ph.D. thesis, Inha University, Republic of Korea (2010)

  38. Ryu, J.K.: An ontology driven intelligent healthcare system with wearable sensors. Ph.D. thesis, Inha University, Republic of Korea, (2012)

  39. Kim, S.H., Chung, K.Y.: 3D simulator for stability analysis of finite slope causing plane activity. Multimed Tools Appl. 68(2), 455–463 (2014)

    Article  Google Scholar 

  40. Kim, J.H., Kim, J., Lee, D., Chung, K.Y.: Ontology driven interactive healthcare with wearable sensors. Multimed. Tools Appl. 71(2), 827–841 (2014)

    Article  MathSciNet  Google Scholar 

  41. Kim, J.H., Chung, K.Y.: Ontology-based healthcare context information model to implement ubiquitous environment. Multimed. Appl. 71(2), 873–888 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by a Grant (14CTAP-C078863-01) from Infrastructure and transportation technology promotion research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kyungyong Chung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, H., Chung, K. Ontology-driven slope modeling for disaster management service. Cluster Comput 18, 677–692 (2015). https://doi.org/10.1007/s10586-015-0424-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-015-0424-1

Keywords

Navigation