Research on the Location of Fire Station Based on GIS and GA

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Abstract:

To locate the fire station under a prescribed period is of strategic significance in the urban fire planning. On the .Net platform, integrate the Genetic Algorithm and Geographic Information System (GIS) via the C# language to resolve the complicated issue of the spatial site selection. The GIS spatial data will be imported to the module first. Then on the basis of both the spatial analysis feature and the network analysis feature of GIS, calculate the individual’s fitness in the process of the genetic evolution. The Conventional coding approach is generally utilized to optimize the small-scale data set. However, to meet the search requirements in the massive spatial data of GIS, the object oriented coding approach is designed in the module. It is indicated that the entire module is able to resolve the site selection issue of the urban fire station properly, which meets the quick response’s requirements.

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377-380

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October 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Masood A. Badri, Amr K. Mortagy, Colonel Ali Alsayed. A multi-objective model for locating fire stations [J]. European Journal of Operational Research. 1998, 110 (2):243-260.

DOI: 10.1016/s0377-2217(97)00247-6

Google Scholar

[2] Church R L, Stores D M, Davis F R. Reserve selection as a maximal covering location problem[J]. Biological Conservation, 1996, 76:105-12.

DOI: 10.1016/0006-3207(95)00102-6

Google Scholar

[3] Church R L. Location modeling and GIS. In:P A Longley, M F Goodchild, D J Maguire et al. (eds), Geographical Information Systems:Volume 1. New York:john Wiley & Sons, Inc, 1999. 293-303.

Google Scholar

[4] Li Xia, Yeh A G O. Integration of genetic algorithms and GIS[J]. International Journal of Geographical Information Science, 2005, 19:581-601.

DOI: 10.1080/13658810500032388

Google Scholar

[5] Tong D Q, Murray A T, Xiao N. Heuristics in Spatial Analysis:A Genetic Algorithm for Coverage Maximization[J]. Annals of the Association of American Geographers, 2009, 99 (4):698-711.

DOI: 10.1080/00045600903120594

Google Scholar

[6] Ferreira C. Gene Expression Programming:A New Adaptive Algorithm for Solving Problems[J]. Complex Systems, 2001, 13(2):87-129.

Google Scholar