%0 Conference Proceedings
%4 sid.inpe.br/mtc-m16c/2019/01.03.12.03
%2 sid.inpe.br/mtc-m16c/2019/01.03.12.03.56
%@issn 2179-4847
%T A performance comparison between two GIS multi-criteria decision aid methods: a case study of desertification evaluation
%D 2018
%A Queiroz,
%A , Heithor Alexandre de Araujo,
%A ,
%A Dantas,
%A , Bruno Cardoso,
%A ,
%A Silva-Neto,
%A , Ciicero Fidelis da,
%A ,
%A Pereira, Thiago Emmanuel,
%A Lima,
%A , Ricardo da Cunha Correia,
%@affiliation Instituto Nacional do Semiárido (INSA)
%@affiliation Instituto Nacional do Semiárido (INSA)
%@affiliation Instituto Nacional do Semiárido (INSA)
%@affiliation Universidade Federal de Campina Grande (UFCG)
%@affiliation Instituto Nacional do Semiárido (INSA)
%@affiliation Universidade Federal de Campina Grande (UFCG)
%@affiliation Universidade Federal de Campina Grande (UFCG)
%@affiliation Instituto Nacional do Semiárido (INSA)
%E Vinhas, Lúbia (INPE),
%E Campelo, Claudio (UFCG),
%B Simpósio Brasileiro de Geoinformática, 19 (GEOINFO)
%C Campina Grande
%8 05-07 dez. 2018
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 122-127
%X Desertification is widely recognized as one of the most relevant environmental problems to be evaluated. In many cases, it requires processing large amounts of data and is also computing intensive. The present study sheds light on this problem in the context of a desertification analysis of the Brazilian Semiarid, using the PROMETHEE Multi-Criteria Decision Aid method, which is a multicriteria analysis method used to identify the outranking relation for a pair of alternatives tackling spatial problems such as site selection problem and land use/suitability analysis. We describe the design and implementation of a practical solution to this problem, based on state-of-the-art theoretical advances and further improvements to deal with large datasets. We compare the performance of our solution with the GRASS software environment. The performance evaluation indicates that our solution can address the problem; it is up to 720 times faster than the GRASS alternative, for the evaluated scenario.
%@language pt
%3 p12.pdf