In this thesis, stochastic methods are utilized to improve a familiar comprehensive probabilistic model to obtain realistic estimates of the earthquake insurance premium rates in different seismic zones of Turkey. The model integrates the information on future earthquake threat with the information on expected earthquake damage to buildings. The quantification of the future earthquake threat is achieved by making use of the seismic hazard analysis techniques. Due to the uncertainties involved, the hazard that may occur at a site during future earthquakes has to be treated in a probabilistic manner. Accessibility of past earthquake data from a number of different data sources, encourages the consideration of every single earthquake report. Seismic zonation of active earthquake generating regions has been improved as recent contributions are made available. Finally, up-to-date data bases have been utilized to establish local attenuation relationships reflecting the expected earthquake wave propagation and its randomness more effectively. The damage that may occur to structures during future earthquakes involves various uncertainties and also has to be treated in a probabilistic manner. For this purpose, damage probability matrices (DPM), expressing what will happen to buildings, designed according to some particular set of requirements, during earthquakes of various intensities, are constructed from observational and estimated data. With the above considerations, in order to demonstrate the application of the improved probabilistic method, earthquake insurance premium rates are computed for reinforced concrete and masonry buildings constructed in different seismic zones of Turkey.

Thesis (D.P.H.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 290-305). / Microfiche. / xix, 305 leaves, bound ill., maps 29 cm

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Prompt and efficient intervention is vital in reducing casualty figures during epidemic outbreaks, disasters, sudden civil strife or terrorism attacks. This can only be achieved if there is a fit-for-purpose and location-specific emergency response plan in place, incorporating geographical, time and vehicular capacity constraints. In this research, a comprehensive emergency response model for situations of uncertainties (in locations' demand and available resources), typically obtainable in low-resource countries, is designed. It involves the development of algorithms for optimizing pre-and post-disaster activities. The studies result in the development of four models: (1) an adaptation of a machine learning clustering algorithm, for pre-positioning depots and emergency operation centers, which optimizes the placement of these depots, such that the largest geographical location is covered, and the maximum number of individuals reached, with minimal facility cost; (2) an optimization algorithm for routing relief distribution, using heterogenous fleets of vehicle, with considerations for uncertainties in humanitarian supplies; (3) a genetic algorithm-based route improvement model; and (4) a model for integrating possible new locations into the routing network, in real-time, using emergency severity ranking, with a high priority on the most-vulnerable population. The clustering approach to solving dept location-allocation problem produces a better time complexity, and the benchmarking of the routing algorithm with existing approaches, results in competitive outcomes.

Thesis (M.P.A.)--Missouri State University, 2008. / "May 2008." Includes bibliographical references (leaves 159-163). Also available online.