946 resultados para Natural disaster warning systems.
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Two sources of bias arise in conventional loss predictions in the wake of natural disasters. One source of bias stems from neglect of accounting for animal genetic resource loss. A second source of bias stems from failure to identify, in addition to the direct effects of such loss, the indirect effects arising from implications impacting animal-human interactions. We argue that, in some contexts, the magnitude of bias imputed by neglecting animal genetic resource stocks is substantial. We show, in addition, and contrary to popular belief, that the biases attributable to losses in distinct genetic resource stocks are very likely to be the same. We derive the formal equivalence across the distinct resource stocks by deriving an envelope result in a model that forms the mainstay of enquiry in subsistence farming and we validate the theory, empirically, in a World-Society-for-the-Protection-of-Animals application
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http://digitalcommons.colby.edu/atlasofmaine2008/1020/thumbnail.jpg
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Natural disasters can cause extensive damage to communities and infrastructure. The state of Maine is fairly lucky because natural disasters are relatively infrequent. Maine does, however, experience earthquakes, flooding, hurricanes, and landslides. Certain areas of the state are more prone to experience natural disaster than others. Using GIS analysis, we are analyzing natural disaster hotspots in Maine to determine if there is a statistically significant relationship between natural disaster susceptibility and socioeconomic variables including income and population.
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Includes bibliography
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Includes bibliography
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Incluye Bibliografía
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In order to handle Natural disasters, emergency areas are often individuated over the territory, close to populated centres. In these areas, rescue services are located which respond with resources and materials for population relief. A method of automatic positioning of these centres in case of a flood or an earthquake is presented. The positioning procedure consists of two distinct parts developed by the research group of Prof Michael G. H. Bell of Imperial College, London, refined and applied to real cases at the University of Bologna under the coordination of Prof Ezio Todini. There are certain requirements that need to be observed such as the maximum number of rescue points as well as the number of people involved. Initially, the candidate points are decided according to the ones proposed by the local civil protection services. We then calculate all possible routes from each candidate rescue point to all other points, generally using the concept of the "hyperpath", namely a set of paths each one of which may be optimal. The attributes of the road network are of fundamental importance, both for the calculation of the ideal distance and eventual delays due to the event measured in travel time units. In a second phase, the distances are used to decide the optimum rescue point positions using heuristics. This second part functions by "elimination". In the beginning, all points are considered rescue centres. During every interaction we wish to delete one point and calculate the impact it creates. In each case, we delete the point that creates less impact until we reach the number of rescue centres we wish to keep.
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Mode of access: Internet.
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Federal Railroad Administration, Office of Rail Safety Research, Washington, D.C.
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Federal Highway Administration, Baton Rouge, Fla.
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Transportation Systems Center, Cambridge, Mass.
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Transportation Systems Center, Cambridge, Mass.