{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/20854"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/20854","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Simulating mobile objects in dynamic landscape processes","abstract":"Geographical Modeling Systems (GMS) are computer based, dynamic landscape simulation tools. As computing power continues to become cheaper and faster, GMSs will become increasingly important for the intelligent management of landscapes. However, a number of technical challenges must be met before GMS capabilities are widely accepted, several of which are addressed in this research. First, because landscape processes occur and are modeled at a variety of spatio-temporal scales, it is necessary to support simultaneous simulation of disparate scales. Second, current landscape decision support systems lack the ability to simulate the behavior of individuals. Simulation modeling of populations is unsuitable for very low population densities where the location of individuals on a large, diverse, and fragmented terrain is important. Finally, in a management setting, techniques for linking distinct landscape models to run simultaneously are necessary. All of these capabilities should run on a common platform with a consistent user interface. More importantly, each submodel should relax its requirement to hold the landscape constant and instead share dynamically varying states between submodels. This effort develops fundamental approaches with prototypes for the simulation of mobile entities within dynamic landscapes.","abstract_html":"Geographical Modeling Systems (GMS) are computer based, dynamic landscape simulation tools. As computing power continues to become cheaper and faster, GMSs will become increasingly important for the intelligent management of landscapes. However, a number of technical challenges must be met before GMS capabilities are widely accepted, several of which are addressed in this research. First, because landscape processes occur and are modeled at a variety of spatio-temporal scales, it is necessary to support simultaneous simulation of disparate scales. Second, current landscape decision support systems lack the ability to simulate the behavior of individuals. Simulation modeling of populations is unsuitable for very low population densities where the location of individuals on a large, diverse, and fragmented terrain is important. Finally, in a management setting, techniques for linking distinct landscape models to run simultaneously are necessary. All of these capabilities should run on a common platform with a consistent user interface. More importantly, each submodel should relax its requirement to hold the landscape constant and instead share dynamically varying states between submodels. 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