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Predicting Drought in the United States Through Spatio-Temporal Ordinal Regression

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abstract
The United States Drought Monitor (USDM) is a discrete measure of drought intensity comprised of six ordered levels, and has been used to classify drought on a weekly basis throughout the continental United States. Environmental covariates representing air temperature, precipitation, and soil moisture are used to create predictive models of the USDM from approximately 2004 - 2014 for the continental United States west of Nevada's eastern border. This is done through a latent Gaussian variable augmentation Bayesian network with parameter expansion. Four models are described to measure the efficiency and significance of modeling the environmental covariates, including a spatially distributed auto-correlation effect, and training the data on the full ten years of data versus only five. Four predictive periods are further compared representing different seasons for a total of sixteen models. Comparisons between these models are made using different formulations of ranked probability scores.
subject
latent Gaussian augmentation
auto-correlation
Bayesian hierarchical models
drought
ordinal regression
spatial random effects
contributor
Wolodkin, Daniel (author)
Erhardt, Robert (committee chair)
Berenhaut, Kenneth (committee member)
Jadhav, Sneha (committee member)
date
2021-06-03T08:36:13Z (accessioned)
2021-06-03T08:36:13Z (available)
2021 (issued)
degree
Mathematics and Statistics (discipline)
identifier
http://hdl.handle.net/10339/98818 (uri)
language
en (iso)
publisher
Wake Forest University
title
Predicting Drought in the United States Through Spatio-Temporal Ordinal Regression
type
Thesis

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