طراحي و اجراي GIS كاربردي جهت مكان يابي شهرك هاي صنعتي با استفاده از مدل هاي فازي، وزن هاي نشان گر و ژنتيك

Design and Implementation of an Applied GIS for Industrial Estates Site Selection using Fuzzy, Weight of Evidence and Genetic Methods

Different industrial decision makers and managers in our country, try to select and organize locations for aggregating industrial units, estates and areas with respect to land use planning visions and industrial development strategies. In this regard, considering large quantity of the input data and diverse criteria affecting this application, it is complicated and difficult to optimally make decision. Geographical Information System (GIS) is able to provide the decision-makers with appropriate tools for removing the difficulties and complexities of site selection process. For this purpose, spatial functions allow selecting an optimum site by using input maps through aggregated operators. These functions are categorized into various groups such as Boolean, Index overlay, Fuzzy logic, Genetic and Weighted evidence, with respect to their activities. So, it is essential to determine appropriate aggregated functions for designing and implementing an applied GIS for selecting optimum industrial estate. In this paper, first, conditions and factors influencing industrial estates site selection are determined and made ready for being entered into aggregated functions. Then, different properties of Index overlay, Fuzzy, Genetic and Weighted evidence models are evaluated for functional efficiency and output accuracy results. Finally, considering the results obtained from different stages, the appropriate model is determined and desirable user interface is developed. As a result of implementing, it was found that Index overlay model with 2.2 seconds time ratio and 5 positive efficiency is the best model for aggregating industrial estate site selection parameters as compared to Fuzzy, Genetic and Weighted evidence models. Keywords: Land Use Planning, GIS, Site Selection, Index overlay, Fuzzy, Genetic and Weight of evidence