A spatial fuzzy influence diagram for modelling spatial objects’ dependencies: a case study on tree-related electric outages

References

• Aspinall, R., 1992. An inductive modelling procedure based on Bayes’ theorem for analysis of pattern in spatial data. International Journal of Geographical Information Science, 6 (2), 105–121. doi:10.1080/02693799208901899
   Web of Science ®Google Scholar
• Chang, S., et al., 2007. Infrastructure failure interdependencies in extreme events: power outage consequences in the 1998 Ice Storm. Natural Hazards, 41, 337–358. doi:10.1007/s11069-006-9039-4
   Web of Science ®Google Scholar
• Coutts, M.P., Nielsen, C.C.N., and Nicoll, B.C., 1999. The development of symmetry, rigidity and anchorage in the structural root system of conifers. Plant and Soil, 217, 1–15. doi:10.1023/A:1004578032481
   Web of Science ®Google Scholar
• Croft, J., 2008. East Cascades (EC) Variant overview, forest vegetation simulator [online]. USA: Department of agriculture, forest service. Available from: http://www.fs.fed.us/fmsc/ftp/fvs/docs/overviews/FVSec_Overview.pdf [Accessed 15 Mar 2016].
   Google Scholar
• Crow, P., 2005. The influence of soils and species on tree root depth [online]. Forestry Commission. Available from: http://www.forestry.gov.uk/pdf/FCIN078.pdf/$FILE/FCIN078.pdf [Accessed 15 Mar 2016].
   Google Scholar
• Espada, R.J., Apan, A., and McDougall, K., 2015. Vulnerability assessment and interdependency analysis of critical infrastructures for climate adaptation and flood mitigation. International Journal of Disaster Resilience in the Built Environment, 6 (3), 313–346. doi:10.1108/IJDRBE-02-2014-0019
   Google Scholar
• Fisher, P., Comber, A., and Wadsworth, R., 2006. Approaches to uncertainty in spatial data. In: R. Devillers and R. Jeansoulin, eds. Fundamentals of spatial data quality. London: ISTE, 43–59.
   Google Scholar
• Guggenmoos, S., 2003. Effects of tree mortality on power line security. Journal of Arboriculture, 29 (4), 181–196.
   Google Scholar
• Guggenmoos, S., 2011. Tree-related electric outages due to wind loading. Arboriculture & Urban Forestry, 37 (4), 147–151.
   Google Scholar
• Howard, R.A. and Matheson, J.E., 2005. Influence diagrams. Decision Analysis, 2 (3), 127–143. doi:10.1287/deca.1050.0020
   Google Scholar
• Janney, G., 2008. The Southern (SN) Variant overview, forest vegetation simulator [online]. USA: Department of agriculture, forest service. Available from: http://www.fs.fed.us/fmsc/ftp/fvs/docs/overviews/FVSsn_Overview.pdf [Accessed 15 Mar 2016].
   Google Scholar
• Kaufmann, A. and Gupta, M.M., 1985. Introduction to fuzzy arithmetic: theory and applications. New York: Van Nostrand Reinhold.
   Google Scholar
• Kripakaran, P., et al., 2014. Performance of power system stabilizer using fuzzy logic controller. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 9 (3), 42–49.
   Google Scholar
• Li, L., Wang, J.F., and Leung, H., 2010. Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk. International Journal of Geographical Information Science, 24 (12), 1759–1784. doi:10.1080/13658816.2010.510473
   Web of Science ®Google Scholar
• Longley, P., et al., 2005. Geographic information systems and science. Chichester: John Wiley & Sons.
   Google Scholar
• Mateou, N.H., Hadjiprokopis, A.P., and Andreou, A.S., 2005. Fuzzy influence diagrams: an alternative approach to decision making under uncertainty. In: M. Mohammadian, ed. Proceedings on the computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce, international conference. Vienna: IEEE Xplore Digital Library.
   Google Scholar
• McDaniels, T., et al., 2007. Empirical framework for characterizing infrastructure failure interdependencies. Journal of Infrastructure Systems, 13 (3), 175–184. doi:10.1061/(ASCE)1076-0342(2007)13:3(175)
   Web of Science ®Google Scholar
• Neapolitan, R.E., 2004. Learning bayesian networks. Chicago: Pearson Prentice Hall.
   Google Scholar
• Pyatt, G., 2001. An ecological classification for forestry in Great Britain. Edinburgh: Forestry Commission.
   Google Scholar
• Rautvuori, M., 2014. ALVAR-toimikunnat rasvaavat alueellisen varautumisen rattaita. National Emergency Supply Agency. Available from: https://www.varmuudenvuoksi.fi/aihe/toimintaymparisto/113/alvar-toimikunnat_rasvaavat_alueellisen_varautumisen_rattaita [Accessed 21 Mar 2017].
   Google Scholar
• Rees, W.T., et al., 1994. Priority trimming to improve reliability. In: ISA conference presentation, Halifax, Nova Scotia. Available from: http://www.ecosync.com/tdworld/PRIOTRIM03.pdf [Accessed 15 Mar 2016].
   Google Scholar
• Rinaldi, S.M., Peerenboom, J.P., and Kelly, T.K., 2001. Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21 (6), 11–25. doi:10.1109/37.969131
   Web of Science ®Google Scholar
• Sawhney, A., Bhatia, R., and Mahajan, P., 2014. Congestion control in wireless communication network using fuzzy logic and machine learning techniques. International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, 3 (11), 12825–12832. doi:10.15662/ijareeie
   Google Scholar
• Sicat, R.S., Emmanuel, J.M.C., and Uday, B.N., 2005. Fuzzy modeling of farmers’ knowledge for land suitability classification. Agricultural Systems, 83 (1), 49–75. doi:10.1016/j.agsy.2004.03.002
   Web of Science ®Google Scholar
• Simpson, P. and Bossuyt, R.V., 1996. Tree-caused electric outages. Journal of Arboriculture, 22 (3), 117–121.
   Google Scholar
• Stassopoulou, A., Petrou, M., and Kittler, J., 1998. Application of a Bayesian network in a GIS based decision making system. International Journal of Geographical Information Science, 12 (1), 23–46. doi:10.1080/136588198241996
   Web of Science ®Google Scholar
• Tatman, J.A. and Shachter, R.D., 1990. Dynamic programming and influence diagrams. IEEE Transactions on Systems, Man, and Cybernetics, 20, 2. doi:10.1109/21.52548
   Web of Science ®Google Scholar
• Teodorović, D., 1999. Fuzzy logic systems for transportation engineering: the state of the art. Transportation Research, part A (33), 337–363.
   Google Scholar
• Utne, I.B., Hokstad, P., and Vatn, J., 2011. A method for risk modelling of interdependencies in critical infrastructures. Reliability Engineering and System Safety, 96, 671–678. doi:10.1016/j.ress.2010.12.006
   Web of Science ®Google Scholar
• Wang, S., et al., 2013. CyberGIS software: a synthetic review and integration roadmap. International Journal of Geographical Information Science, 27 (11), 2122–2145. doi:10.1080/13658816.2013.776049
   Web of Science ®Google Scholar
• Wang, S., Liu, Y., and Padmanabhan, A., 2015. Open CyberGIS software for geospatial research and education in the big data era. SoftwareX, 5, 1–5. doi:10.1016/j.softx.2015.10.003
   Google Scholar
• Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8 (3), 338–353. doi:10.1016/S0019-9958(65)90241-X
   Google Scholar
• Zadeh, L.A., 1996. The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems. BT Technology Journal, 14 (4), 32–36.
   Web of Science ®Google Scholar
• Zhang, Z., et al., 2014. A fuzzy multiple-attribute decision making modelling for vulnerability analysis on the basis of population information for disaster management. International Journal of Geographical Information Science, 28 (9), 1922–1939. doi:10.1080/13658816.2014.908472
   Web of Science ®Google Scholar
• Zhao, J. and Bose, B.K., 2002. Evaluation of membership functions for fuzzy logic controlled induction motor drive. IECON 02, IEEE 2002 28th Annual Conference of the Industrial Electronics Society, 1, 229–234.
   Google Scholar
• Zhi, Z. and Jin, F.X., 2010. Application study on fuzzy influence diagram in highway tunnel construction safety risk assessment. In: proceeding of 2010 international conference on management science & engineering. Melbourne: IEEE Xplore digital Library.
   Google Scholar
• Zhu, A.X., et al., 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal, 65 (5), 1463–1472. doi:10.2136/sssaj2001.6551463x
   Web of Science ®Google Scholar
• Zhu, X., Aspinall, R.J., and Healey, R.G., 1996. ILUDSS: a knowledge-based spatial decision support system for strategic land-use planning. In Computers and Electronics in Agriculture, 15, 279–301. doi:10.1016/0168-1699(96)00022-1
   Web of Science ®Google Scholar
• Zimmermann, H.J., 2001. Fuzzy set theory and its application. London: Kluwer Academic.
   Google Scholar