The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
https://doi.org/10.5194/isprs-archives-XLII-4-W4-463-2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-463-2017
27 Sep 2017
 | 27 Sep 2017

GIS-BASED SITE SELECTION FOR UNDERGROUND NATURAL RESOURCES USING FUZZY AHP-OWA

A. R. Sabzevari and M. R. Delavar

Keywords: Site Selection, Natural Resource, Multi-Criteria Decision Making, Ordered Weighted Average, Fuzzy AHP, Fuzzy AHP-OWA

Abstract. Fuel consumption has significantly increased due to the growth of the population. A solution to address this problem is the underground storage of natural gas. The first step to reach this goal is to select suitable places for the storage. In this study, site selection for the underground natural gas reservoirs has been performed using a multi-criteria decision-making in a GIS environment. The “Ordered Weighted Average” (OWA) operator is one of the multi-criteria decision-making methods for ranking the criteria and consideration of uncertainty in the interaction among the criteria. In this paper, Fuzzy AHP_OWA (FAHP_OWA) is used to determine optimal sites for the underground natural gas reservoirs. Fuzzy AHP_OWA considers the decision maker’s risk taking and risk aversion during the decision-making process. Gas consumption rate, temperature, distance from main transportation network, distance from gas production centers, population density and distance from gas distribution networks are the criteria used in this research. Results show that the northeast and west of Iran and the areas around Tehran (Tehran and Alborz Provinces) have a higher attraction for constructing a natural gas reservoir. The performance of the used method was also evaluated. This evaluation was performed using the location of the existing natural gas reservoirs in the country and the site selection maps for each of the quantifiers. It is verified that the method used in this study is capable of modeling different decision-making strategies used by the decision maker with about 88 percent of agreement between the modeling and test data.