Advertisement

Complex Geoinformation Analysis of Multiple Natural Hazards Using Fuzzy Logic

  • Valentina Nikolova
  • Plamena ZlatevaEmail author
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Natural hazards are existence of natural components and processes, which create a situation that could negatively affect people, the economy and the environment. Rising public awareness about natural hazards could improve the quality of life, save financial resources and even save lives. The complicated essence of natural hazards and the interrelations between natural components require a complex analysis of natural hazard factors. In the current research, the factors are analysed by application of analytic hierarchy process. Spatial overlay analysis is done in GIS environment and as a result, landslide susceptibility and flood susceptibility maps are created. Single hazard maps are overlaid in order to receive a complex hazard level. A fuzzy logic with four inputs and one output is designed. This model is used to determine the complex level of hazard considering the factors interaction. The results of the current research and suggested approach could support decision makers in civil protection, territorial planning and management.

References

  1. Arnoldus HML (1980) An approximation of rainfall factor in the Universal Soil Loss Equation. In: De Boodt M, Gabriels D (eds) Assessment of erosion. Wiley, Chichester, pp 127–132Google Scholar
  2. Climate data for cities worldwide (2018) Climate data. https://en.climate-data.org. Accessed 22 Nov 2017
  3. CORINE Land Cover project (2012). http://eea.government.bg/bg/projects/korine-14/index. Accessed 25 Apr 2016
  4. Costanzo D, Rotigliano E, Irigaray C, Jimenez-Peralvarez JD, Chacon J (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci, 12:327–340.  https://doi.org/10.5194/nhess-12-327-2012CrossRefGoogle Scholar
  5. Costea M (2012) Using the Fournier indexes in estimating rainfall erosivity. Case study-the Secasul Mare Basin. Aerul si Apa. Componente ale Mediului, pp 313–320Google Scholar
  6. Di Mauro C, Bouchon S, Carpignano A, Golia E, Peressin S (2006) Definition of multi-risk maps at regional level as management tool: Experience gained by civil protection authorities of Piemonte region. In: Proceedings of the 5th Conference on risk assessment and management in the civil and industrial settlements. http://conference.ing.unipi.it/vgr2006/archivio/Archivio/2006/Articoli/700196.pdf. Accessed 17 Sept 2017
  7. Eshrati L, Mahmoudzadeh M, Taghvaei M (2015) Multi hazards risk assessment, a new methodology. Int J Health Syst Disaster Manag 3(2):79–88Google Scholar
  8. Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221CrossRefGoogle Scholar
  9. Flood Risk Management Plan, Black Sea Directorate—Varna (2018) BSBD. https://www.bsbd.org/uk/FR_mplans.html. Accessed 5 Feb 2018
  10. Fotis M, Georgia M, Apostolos V (2012) Estimation of the Prefecture of Evros vulnerability in flood cases using GIS and fuzzy set algebra. Paper presented at the 16th international water technology conference, Istanbul, Turkey, 7–10 May 2012Google Scholar
  11. Gilat, A. (2011) MATLAB—An Introduction with Applications. 4-th ed., Wiley, New YorkGoogle Scholar
  12. Gill JC, Malamud BD (2014) Reviewing and visualizing the interactions of natural hazards. Rev Geophys 52:680–722CrossRefGoogle Scholar
  13. Gill J C, Malamud BD (2016) Hazard interactions and interaction networks (cascades) within multi-hazard methodologies. Earth Syst. Dynam. 7:659–679.  https://doi.org/10.5194/esd-7-659-2016, www.earth-syst-dynam.net/7/659/2016CrossRefGoogle Scholar
  14. Grozavu A, Margarint MC, Patriche CV (2010) GIS applications for landslide susceptibility assessment: a case study in Iaşi County (Moldavian Plateau, Romania). Trans Inf Commun Technol 43:393–404.  https://doi.org/10.2495/RISK100341CrossRefGoogle Scholar
  15. Harab MM, Dell’Acqua F (2017) Remote Sensing in Multi-Risk Assessment: Improving disaster preparedness. IEEE Geosc Remote Sens Mag 5(1):53–65.  https://doi.org/10.1109/MGRS.2016.2625100CrossRefGoogle Scholar
  16. Ilanloo M (2011) A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran. Proced Soc Behav Sci 19:668–676CrossRefGoogle Scholar
  17. Jiang W, Deng L, Chen L, Wu J, Li J (2009) Risk assessment and validation of flood disaster based on fuzzy mathematics. Prog Nat Sci 19:1419–1425CrossRefGoogle Scholar
  18. Kanchev I (1995a) Geological map of Bulgaria, Map sheet Sliven. Scale 1:100000. Geology and Geophysics JSCGoogle Scholar
  19. Kanchev I (1995b) Geological map of Bulgaria, map sheet Sungurlare. Scale 1:100000. Geology and Geophysics JSCGoogle Scholar
  20. Karagiozi E, Fountoulis I, Konstantinidis A, Andreadakis E, Ntouros K (2011) Flood hazard assessment based on geomorphological analysis with GIS tools—the case of Laconia (Peloponnesus, Greece). In: Proceedings of the 8th international symposium on GIS, Ostrava 2011, pp 201–216Google Scholar
  21. Kazakis N, Kougias I, Patsialis T (2015) Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope-Evros region. Greece Sci Total Environ 538:555–563CrossRefGoogle Scholar
  22. Kayastha P, Bijukchhen SM, Dhital MR, De Smedt F (2013) GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola Area, Eastern Nepal. J Geol Soc India 82:249–261CrossRefGoogle Scholar
  23. Kourgialas NN, George P, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas—a case study. Hydrol Sci J 56(2):212–225.  https://doi.org/10.1080/02626667.2011.555836CrossRefGoogle Scholar
  24. Kreibich H, Bubeck P, Kunz M, Mahlke H, Parolai S, Khazai B, Daniell J, Lakes T, Schroter K (2014) A review of multiple natural hazards and risks in Germany. Nat Hazards 74:2279–2304CrossRefGoogle Scholar
  25. Lari S, Frattini P, Crosta GB (2014) A probabilistic approach for landslide hazard analysis. Eng Geol 182:3–14CrossRefGoogle Scholar
  26. Liu B, Siu Y L, Mitchell G (2016a) Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment. Nat Hazards Earth Syst Sci 16:629–642.  https://doi.org/10.5194/nhess-16-629-2016CrossRefGoogle Scholar
  27. Liu B, Siu YL, Mitchell G, Xu W (2016b) The danger of mapping risk from multiple natural hazards. Nat Hazard 82:139–153.  https://doi.org/10.1007/s11069-016-2184-5CrossRefGoogle Scholar
  28. Nikolova V, Zlateva P (2017) Assessment of flood vulnerability using fuzzy logic and geographical information systems. In: Murayama Y, Velev D, Zlateva P et al (eds) Information technology in disaster risk reduction 2016, IFIP Advances in information and communication technology, vol 501. Springer, Cham, pp 254–265.  https://doi.org/10.1007/978-3-319-68486-4_20Google Scholar
  29. Pandey AR, Shahbodaghlou F (2014) Landslide hazard mapping of Nagadhunga-Naubise section of the Tribhuvan highway in Nepal with GIS application. J Geogr Inf Syst 6:723–732Google Scholar
  30. Perera EDP, Lahat L (2015) Fuzzy logic based on flood forecasting model for the Kelantan river basin, Malaysia. J Hydro-environ Res 9:542–553CrossRefGoogle Scholar
  31. Prevention Web (2018) Strengthening climate change adaptation through effective disaster risk reduction. http://www.preventionweb.net. Accessed 6 Sept 2018
  32. Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void filling interpolation methods for SRTM data. Int J Geogr Inf Sci 21(9):983–1008CrossRefGoogle Scholar
  33. Rosso R, Rulli M C, Vannucchi G (2006) A physically based model for the hydrologic control on shallow landsliding. Water Resource Research 42, W 06410.  https://doi.org/10.1029/2005wr004369
  34. Saaty RW (1987) The analytic hierarchy process—what it is and how it is used. Math Model 9(3–5):161–176CrossRefGoogle Scholar
  35. Tazik E, Jahantab Z, Bakhtiari M, Rezaei A, Alavipanah S K (2014) Landslide susceptibility mapping by combining the three methods: Fuzzy logic, Frequency ratio and Analytical hierarchy process in Dozain basin. Paper presented at the 1st ISPRS International Conference on Geospatial Information Research, 15–17 November 2014, Tehran, Iran. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol XL-2/W3.  https://doi.org/10.5194/isprsarchives-XL-2-W3-267-2014CrossRefGoogle Scholar
  36. Tzvetkova S (2017) Ecological aspects of the management of transport enterprises. Scientific Journal Mechanics. Transport and Communications 15 (3/3): IX-19—IX-25Google Scholar
  37. United National Office for Disaster Risk Reduction (2018) UNISDR Annual report 2017. https://www.unisdr.org/files/58158_unisdr2017annualreport.pdf. Accessed 6 Sept 2018
  38. Velev D, Zlateva P (2018) Information system framework for integrated risk assessment from natural disasters. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives 42 (3W4): 535–541Google Scholar
  39. Yeganeh N, Sabri S (2014) Flood vulnerability assessment in Iskandar Malaysia using multi-criteria evaluation and fuzzy logic. Res J Appl Sci Eng Technol 8(16):1794–1806CrossRefGoogle Scholar
  40. Zheng N, Tachikawa Y, Takara K (2008) A Distributed flood inundation model integrating with rainfall-runoff processes using GIS and remote sensing data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. vol XXXVII, Part B4. Beijing, pp 1513–1518Google Scholar
  41. Zlateva P, Velev D (2013) Complex risk analysis of natural hazards through fuzzy logic. J Adv Manag Sci 1(4):395–400CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Geology and GeoinformaticsUniversity of Mining and GeologySofiaBulgaria
  2. 2.Institute of Robotics, Bulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations