Skip to main content
SpringerLink
Log in

Situation Diagnosis Based on the Spatially-Distributed Dynamic Disaster Risk Assessment

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing IV (CSIT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1080))

Included in the following conference series:

Abstract

A dynamic spatially-distributed model of integral risk assessment is represented in the paper. A multi-risk for a valuable object is formed as a combination of four components such as danger, threat potential, threat level, and potential losses. In order to provide comparing the risks from different disasters and assess their joint influence on the valuable object in the form of multi-risk a quantitative value of each risk component is proposed to represent in the form of qualitative value using the appropriate scales. A diagnostic method for disaster response operations based on the spatially-distributed model of integral risk assessment is developed. A hybrid algorithm of identification of the situation in disaster conditions using the case-based and rule-based reasoning is described. The experiment examining the validity and efficiency of the proposed hybrid diagnosis method is described. It’s concluded that the proposed method provides sufficient performance for the cell size 5 m and above, so it is acceptable for solving the practical forest fire fighting problems in GIS-based DSS.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Shen, G., Zhou, L., Wu, Y., Cai, Z.: A global expected risk analysis of fatalities, injuries, and damages by natural disasters. Sustainability 10(7), 2573 (2018). https://doi.org/10.3390/su10072573

    Article  Google Scholar 

  2. Thompson, M.P., Haas, J.R., Gilbertson-Day, J.V., Scott, J.H., Langowski, P., Bpwne, E., Calkin, D.: Development and application of a geospatial wildfire exposure and risk calculation tool. Environ. Model Softw. 63, 61–72 (2015). https://doi.org/10.1016/j.envsoft.2014.09.018

    Article  Google Scholar 

  3. Thompson, M.P., Calkin, D.E., Finney, M.A., Ager, A.A., Gilbertson-Day, J.V.: Integrated national-scale assessment of wildfire risk to human and ecological values. Stoch. Environ. Res. Risk Assess. 25(6), 761–780 (2011). https://doi.org/10.1007/s00477-011-0461-0

    Article  Google Scholar 

  4. Thompson, M.P., Zimmerman, T., Mindar, D., Taber, M.: Risk terminology primer: basic principles and glossary for the wildland fire management community. Gen. Tech. Rep. RMRS-GTR-349. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station (2016)

    Google Scholar 

  5. Gallina, V., Torresan, S., Critto, A., Sperotto, A., Glade, T., Marcomini, A.: A review of multi-risk methodologies for natural hazards: consequences and challenges for a climate change impact assessment. J. Environ. Manag. 168, 123–132 (2016). https://doi.org/10.1016/j.jenvman.2015.11.011

    Article  Google Scholar 

  6. World Meteorological Organization (WMO): Comprehensive risk assessment for natural hazards. Geneva: WMO/TD No. 955, Switzerland (1999)

    Google Scholar 

  7. Zharikova, M.: Methodological basis of geoinformation technology of decision support in combined natural and man-made systems in destructive processes conditions. Doctoral Thesis, Ukrainian Academy of Printing, Lviv, Ukraine (2018)

    Google Scholar 

  8. Van Westen, C.J., Shroder, J., Bishop, M.P.: Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise Geomorphol. 3, 259–298 (2013). https://doi.org/10.1016/B978-0-12-374739-6.00051-8

    Article  Google Scholar 

  9. Calkin, D.E., Thompson, M.P., Finney, M.A., Hyde, K.D.: A real-time assessment tool supporting wildland fire decision making. J. For. 109(5), 274–280 (2011)

    Google Scholar 

  10. Finney, M.: Modeling the spread and behavior of prescribed natural fires. In: Proceedings of the 12th Conference on Fire and Forest Meteorology, Jekyll Island, Georgia, pp. 138–143 (1993)

    Google Scholar 

  11. Finney, M.: The challenge of quantitative risk analysis for wildland fire. For. Ecol. Manag. 211(1–2), 97–108 (2005). https://doi.org/10.1016/j.foreco.2005.02.010

    Article  Google Scholar 

  12. Rausand, M., Hoyland, A.: System Reliability Theory: Models, Statistical Methods, and Applications. Wiley-Interscience, Hoboken (2004)

    MATH  Google Scholar 

  13. Kloprogge, P., Van der Sluijs, J., Petersen, A.: A Method for the Analysis of Assumptions in Assessments. Netherlands Environmental Assessment Agency, Bilthoven (2005)

    Google Scholar 

  14. Krishnamoorthi, N.: Role of remote sensing and GIS in natural-disaster management cycle. Imp. J. Interdiscip. Res. 2(3), 144–154 (2016)

    Google Scholar 

  15. Zharikova, M., Sherstjuk, V.: Threat assessment method for intelligent disaster decision support. Advances in Intelligent Systems and Computing, vol. 512, pp. 81–100 (2017). https://doi.org/10.1007/978-3-319-45991-2_6

  16. Balakrishnan, K., Honavar, V.: Intelligent diagnosis systems. J. Intell. Syst. 8(3), 237–290 (1998). https://doi.org/10.1515/JISYS.1998.8.304.239

    Article  Google Scholar 

  17. Cheng, T., Kocka, T., Zhang, N.L.: Effective dimensions of partially observed polytrees. Int. J. Approx. Reason. 38(3), 311–332 (2005). https://doi.org/10.1016/j.ijar.2004.05.008

    Article  MathSciNet  MATH  Google Scholar 

  18. Lucas, P.J.F.: Bayesian model-based diagnosis. Int. J. Approx. Reason. 27(2), 99–119 (2001). https://doi.org/10.1016/S0888-613X(01)00036-6

    Article  MathSciNet  MATH  Google Scholar 

  19. Bhagwat, A.: Knowledge-based service diagnosis system. Int. J. Comput. Sci. Technol. 3(5), 182–184 (2015)

    Google Scholar 

  20. Ward, M.O., Grinstein, G.G., Keim, D.A.: Interactive Data Visualization - Foundations, Techniques, and Applications. A.K. Peters, Ltd., Natick (2010). https://doi.org/10.1201/9780429108433

    Book  MATH  Google Scholar 

  21. Amarosicz, M., Psiuk, K., Rogala, T., Rzydzik, S.: Diagnostic shell expert systems. Diagnostica 17(1), 33–40 (2016)

    Google Scholar 

  22. Tan, C.F., Wahidin, L.S., Khalil, S.N., Tamaldin, N., Hu, J., Rauterberg, G.W.M.: The application of expert system: a review of research and applications. ARPN J. Eng. Appl. Sci. 11(4), 2448–2453 (2016)

    Google Scholar 

  23. Martinez, J., Vega-Garcia, C., Chuvieco, E.: Human-caused wildfire risk rating for prevention planning in Spain. J. Environ. Manag. 90(2), 1241–1252 (2009). https://doi.org/10.1016/j.jenvman.2008.07.005

    Article  Google Scholar 

  24. Martinez, M.V.: Knowledge engineering for intelligent decision support. In: Proceeding of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017), Montreal, pp. 5131–5135 (2017). https://doi.org/10.24963/ijcai.2017/736

  25. Rieck, C.Z., Wu, X.W., Jiang, J.C., Zhu, A.X.: Case-based knowledge formalization and reasoning method for digital terrain analysis – application to extracting drainage networks. Hydrol. Earth Syst. Sci. 20, 3379–3392 (2016). https://doi.org/10.5194/hess-2015-539

    Article  Google Scholar 

  26. Rieck, K., Trinius, P., Willems, C., Holz, T.: Automatic analysis of malware behavior using machine learning. J. Comput. Secur. 19(4), 639–668 (2011)

    Article  Google Scholar 

  27. Symeonidisa, A.L., Chatzidimitriouc, K.C., Athanasiadisd, I.N., Mitkas, P.A.: Data mining for agent reasoning: a synergy for training intelligent agents. Eng. Appl. Artif. Intell. 20(8), 1097–1111 (2007). https://doi.org/10.1016/j.engappai.2007.02.009

    Article  Google Scholar 

  28. Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57(1), 345–420 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  29. Gong, Y., Li, J., Zhou, Y., Li, Y., Chung, H.S., Shi, Y., Zhang, J.: Genetic learning particle swarm optimization. IEEE Trans. Cybern. 46(10), 2277–2290 (2016). https://doi.org/10.1109/TCYB.2015.2475174

    Article  Google Scholar 

  30. Hoffman, R.: Origins of situation awareness: cautionary tales from the history of concepts of attention. J. Cogn. Eng. Decis. Mak. 9(1), 73–83 (2015). https://doi.org/10.1177/1555343414568116

    Article  Google Scholar 

  31. Fogel, D.B., Fogel, L.J., Porto, V.W.: Evolving neural networks. Biol. Cybern. 63(6), 487–493 (1990)

    Article  MATH  Google Scholar 

  32. Allam, A.A., Bakeir, M.Y., Abo-Tabl, E.A.: Some methods for generating topologies by relations. Bull. Malays. Math. Sci. Soc. 31(1), 35–45 (2008)

    MathSciNet  MATH  Google Scholar 

  33. Scott, J.H., Thompson, M.P., Calkin, D.E.: A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station (2013)

    Google Scholar 

  34. Apostolakis, G.E.: How useful is quantitative risk assessment. Risk Anal. 24(3), 515–520 (2004). https://doi.org/10.1111/j.0272-4332.2004.00455.x

    Article  Google Scholar 

  35. Aven, T., Zio, E.: Model output uncertainty in risk assessment. Int. J. Perform. Eng. 9(5), 475–486 (2013)

    Google Scholar 

  36. Andreu, A., Hermansen-Baez, L.A.: Fire in the South 2: the southern wildfire risk assessment. A report by Southern Group of State Forester, 32 p. (2008)

    Google Scholar 

  37. Dubois, D., Prade, H.: Possibility theory, probability theory, and multiple-valued logics: a clarification. Ann. Math. Artif. Intell. 32, 35–66 (2001). https://doi.org/10.1023/A:1016740830286

    Article  MathSciNet  MATH  Google Scholar 

  38. Dubois, D., Prade, H.: What are fuzzy rules and how to use them. Fuzzy Sets Syst. 84(2), 169–185 (1996). https://doi.org/10.1016/0165-0114(96)00066-8

    Article  MathSciNet  MATH  Google Scholar 

  39. Dubois, D., Prade, H.: Possibilistic logic: a retrospective and prospective view. Fuzzy Sets Syst. 144(1), 3–23 (2004). https://doi.org/10.1016/j.fss.2003.10.011

    Article  MathSciNet  MATH  Google Scholar 

  40. Pawlak, Z., Jerzy, W., Slowinski, R., Ziarko, W.: Rough sets. Commun. ACM 38(11), 88–95 (1995). https://doi.org/10.1145/219717.219791

    Article  Google Scholar 

  41. Zharikova, M., Sherstjuk, V.: The hybrid intelligent diagnosis method for the MultiUAV-Based forest fire-fighting response system. In: Proceedings of 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, pp. 339–342 (2018). https://doi.org/10.1109/STC-CSIT.2018.8526609

  42. Zharikova, M., Sherstjuk, V.: Situation diagnosis based on the spatially-distributed dynamic disaster risk assessment In: Proceedings of the International Scientific Conference “Computer sciences and information technologies” (CSIT 2019), vol. 3, pp. 205–209. IEEE (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Maryna Zharikova & Volodymyr Sherstjuk

Authors
  1. Maryna Zharikova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Volodymyr Sherstjuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryna Zharikova .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zharikova, M., Sherstjuk, V. (2020). Situation Diagnosis Based on the Spatially-Distributed Dynamic Disaster Risk Assessment. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing IV. CSIT 2019. Advances in Intelligent Systems and Computing, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_31

Download citation

Publish with us

Policies and ethics

Search

Navigation