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A Rough Set and GIS Based Approach for Selecting Suitable Shelters during an Evacuation Process

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DOI: 10.4236/jgis.2013.51001    4,224 Downloads   7,221 Views   Citations

ABSTRACT

Humanity suffers an ever-present threat of crises. In the event of a crisis, the population in affected areas will be in danger and will need to be evacuated to a safer in order to protect their lives. One of the difficulties in emergency management is quickly and accurately selecting suitably safe areas of refuge. This paper aims to explain an evacuation shelter selection process that uses rough set theory and a geographical information system (GIS). The proposed approach uses rough set theory concepts to classify shelters and selects suitable shelters on the basis of three factors: distance, capacity, and the availability of life requirements. The preparation of data and reporting of results are performed via the GIS environment. The proposed approach was implemented using Masoura,Egypt, as a case study and the re- sults of this implementation are presented.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. S. Elheishy, A. A. Saleh and A. Asem, "A Rough Set and GIS Based Approach for Selecting Suitable Shelters during an Evacuation Process," Journal of Geographic Information System, Vol. 5 No. 1, 2013, pp. 1-12. doi: 10.4236/jgis.2013.51001.

References

[1] Brampton Flower City, “The City of Brampton Emergency Evacuation Plan,” 2011. http://www.Brampton.ca/EN/RESIDENTS/Pages/Welcome.aspx
[2] M. Saadatseresht, A. Mansourian and M. Taleai, “Evacuation Planning Using Multiobjective Evolutionary Optimization Approach,” European Journal of Operation Research, Vol. 198, No. 1, 2008, pp. 305-314. doi:10.1016/j.ejor.2008.07.032
[3] J. Coutinho-Rodrigues, L. Tralhao and L. Alcada-Almeida, “Solving a Location-Routing Problem with a Multiobjective Approach: The Design of Urban Evacuation Plans,” Journal of Transport Geography, Vol. 22, 2012, pp. 206-218. doi:10.1016/j.jtrangeo.2012.01.006
[4] C. Tasi, W. Wang, C. Chen, H. Chen and M. Len, “The Construction of Decision Model for Tourism Disaster Evacuation Based on GIS and Fuzzy Theory,” World Academy of Science, Engineering and Technology, Vol. 54, pp. 82-85, 2009.
[5] Z. Pawlak, “Rough Sets,” International Journal of Computer and Information Sciences, Vol. 11, No. 5, 1982, pp. 341-356.
[6] J. Ponce and A. Karahoca, “Data Mining and Knowledge Discovery in Real Life Applications,” I-Tech, Vienna, 2009.
[7] Department of the Environment, “Handling Geographic Information,” HMSO, London, 1987.
[8] C. Shupeng, L. Xuejun and Z. Chenghu, “Introduction to Geographic Information Systems,” Science Press, Marrickville, 1999.
[9] T. Fangqin and Z. Xin, “A GIS-Based 3D Simulation for Occupant Evacuation in a Building,” Tsinghua a Acience and Technology, Vol. 13, No. S1, 2008, pp. 85-64.
[10] American Red Cross, “ARC 4496—Guidelines for Hurricane,” 2012. http://www.redcross.org
[11] S. Chakrabarti, et al., “Data Mining: Know It All,” Morgan Kaufmann publishing, Burlington, 2009.
[12] Z. Marzuki and F. Ahmad, “Data Mining Discretization Methods and Performances,” Proceedings of the International Conference on Electrical Engineering and Informatics, Institute Teknologi Bandung, Bandung, 17-19 June 2007, pp. 535-537.
[13] Z. Pawlak, “Rough Sets Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, Dordrecht, 1991.

  
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