Global ETD Search
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Showing 1 to 7 of 7 for “"Kriging Models"”.
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Water Level Modeling around German Bight
… spatial. We apply first stochastic time series models to the data on temporal level. The model has four patterns: trend, seasonality, autoregressive components and the heteroscedastic residuals captured by a dynamics conditional volatility model. Two different procedures are applied in this work …
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Multi-fidelity strategies for lean burn combustor design
… Surrogate modeling design strategies, including Kriging models, are currently being used to balance the challenges of accuracy and computational resource to accelerate the combustor design process. However, its feasibility still largely relies on the total number of design variables, objective …
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Weibull mixture model for grouped data and pattern identification in spatial and spatial-temporal data.
… and material science, we develop statistical models and apply statistical tools for characterizing patterns that exist in different types of data. In the first project, we propose the Weibull mixture model to fit the distribution of grain size in continental sediments in geological studies. We …
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Optimizing peak gust and maximum sustained wind speed estimates from mid-latitude wave cyclones
… on an anisotropic (directionally-dependent) kriging interpolation methodology. Overall, wind speed magnitudes and high intensity locations were identified accurately for each storm. Directional trends and wind swaths were also consistently located in appropriate locations based on known storm …
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A response surface model of the air quality impacts of aviation
… Within APMT, there is a desire for faster models that can analyze multiple policy scenarios for decades into the future in order to inform policy decisions on a reasonable time scale. One particular need is that for a fast surrogate air quality model that relates changes in aviation …
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New Opportunities in Crowd-Sourced Monitoring and Non-government Data Mining for Developing Urban Air Quality Models in the US
… [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land …