Strategic management of wildlands for fire is increasingly a mix of traditional firescience, climatology and human perceptions. Not only must managers be expert atmodeling fuels and fire behavior, they must also understand human behavior, and theeffects of climate on landscapes. We hypothiszed that areas in national forests differspatially in their importance to stakeholders, including both the public and landmanagers. That this difference is based upon the inclusion of factors not typically foundin wildland fire models. To test this hypothesis we used a multidimensional approach toassess the spatial variability several factors including recreation, property values and fuelmoisture. This approach combined a geographic information system with the analytichierarchy process to predict and test the current distribution of areas in national forestsimportant to stakeholders.Inclusion of stakeholders appears to improve the validity and useability of aspatial decision support system. Comparing the model created in this dissertation withseveral others demonstrates that it is important to strike the right balance betweenstakeholders and technical experts when designing and creating a model. It is alwaysbeneficial, however, to a significant level of stakeholder involvement.Areas important for fire mitigation efforts depended on the stakeholder oraudience rating the model. Raters from the U.S. Forest Service tended to favor areas withhigh fire probability scores, while those from the Park Service prefered recreation areasand places people value. In both cases, locations people had easy access to, such as alongroads and trails were favored.These results confirmed the hypothesis that areas of importance are differentbased on the individual rating the model. Further testing and refinement of the modelincludes expanding the study area beyond the southwestern United States as wells asobtaining better sources of data with finer spatial resolutions.

There is a recognised need for the continued improvement of both the techniques and technology for spatial decision support in infrastructure site selection. Many authors have noted that current methodologies are inadequate for real-world site selection decisions carried out by heterogeneous groups of decision-makers under uncertainty. Nevertheless despite numerous limitations inherent in current spatial problem solving methods, spatial decision support systems have been proven to increase decision-maker effectiveness when used. However, due to the real or perceived difficulty of using these systems few applications are actually in use to support decision-makers in siting decisions. The most common difficulties encountered involve standardising criterion ratings, and communicating results. This research has focused on the use of Approximate Reasoning to improve the techniques and technology of spatial decision support, and make them easier to use and understand. The algorithm developed in this research (ARAISS) is based on the use of natural language to describe problem variables such as suitability, certainty, risk and consensus. The algorithm uses a method based on type II fuzzy sets to represent problem variables. ARAISS was subsequently incorporated into a new Spatial Decision Support System (InfraPlanner) and validated by use in a real-world site selection problem at Australia's Brisbane Airport. Results indicate that Approximate Reasoning is a promising method for spatial infrastructure planning decisions. Natural language inputs and outputs, combined with an easily understandable multiple decision-maker framework created an environment conducive to information sharing and consensus building among parties. Future research should focus on the use of Genetic Algorithms and other Artificial Intelligence techniques to broaden the scope of existing work.

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Spatial Decision Making as a branch of decision making science deals with geographically related data in order to achieve complex spatial decision problems. Fuzzy set theory is one of the methods that can be used to come up with these types of problems. On the other hand, Geographical Information Systems (GIS) is one of the most powerful tools that we can use to accomplish spatial decision problems. Selection of the suitable site or land-use for the real estate is also a spatial decision making problem. When we consider the initial dynamics of the suitably located property from the point of view of value and potential we observe that the &ldquo / good location&rdquo / is the dominating factor. This study reports on the development of a kind of decision support system for locational suitability of settlements that integrates the fuzzy set (FZ) theory, a rule-based system (RBS) and GIS. This study is thought as the assistant for the property managers that are buyers and sellers. It can function as the property consultant for the buyers when they are looking for a property to buy and also it helps the real estate agencies to sell their properties. On the other hand, different scenarios of the potential areas according to the different user&rsquo / s preferences are depicted and they are joined and compared with the results of the vulnerability to earthquake hazards&rsquo / of the same area. Odunpazari - Eskisehir area is selected for implementation of the case study because of the data availability. As a result of this study, it can be said that most suitable property changes depending on the people&rsquo / s preferences. In addition, it is seen that most of the buildings that are locationally suitable are highly vulnerable to the earthquake hazards.

Integrating decision analytical techniques in geographic information systems (GIS) can help remove the two primary obstacles in spatial decision making: inaccessibility to required geographic data and difficulties in synthesizing various criteria. I developed a GIS model to assist people seeking optimal residential locations. Fuzzy set theory was used to codify criteria for each factor used in evaluating residential locations, and weighted linear combination (WLC) was employed to simulate users' preferences in decision making. Three examples were used to demonstrate the applications in the study area. The results from the examples were analyzed. The model and the ArcGIS Extension can be used in other geographic areas for residential location selection, or in other applications of spatial decision making.

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