Artificial Neural Networks as a Tool for Site Selection within Gis

To obtain more flexibility and more effective capability of handling and processing imprecise information about the real world, fuzzy set theory is introduced into GIS. FuzzyCell is a system designed and implemented to enhance conventional GIS software (ArcMap®) with fuzzy set theory. Extending GIS with fuzzy logic (a linguistic approach as the model of human thinking) not only offers a way to represent and handle uncertainty present in the continuous real world but also assist GIS user to make decisions using experts’ experiences in decision-making process. The cost of finding solutions to decision-making problems by models which enable decision-makers to express their constraints and imprecise concepts that are used with geographic data (i.e., fuzzy logic) for large volume is high. For such cases, artificial neural networks (ANNs), which can solve complex problems and can “learn” from prior applications, can be used. In this study, a fuzzy rule based system (FuzzyCell) was used to model site selection problem by capturing rules from human experts. The ANNs were trained by obtained fuzzy measures against input data to recognize patterns for reproduction of relevant sites for new locations and were tested whether ANNs can produce reasonable guesses for locations other than training sites when compared to results obtained from FuzzyCell. Two metrics, Kolmogorov-Simirnov test metric and root mean squared error values, were used for testing. It was found in this study that ANNs provide reasonable guesses for locations other than training sites. * Corresponding author. Zuhal Akyürek