Visualization and analysis of sensor data for detecting microclimate cold air pools

Graduate Thesis Or Dissertation

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Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/k0698d22b

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Attribute Name	Values
Creator	Singh, Satpreet Harcharan
Abstract	Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like this lead to microclimate anomalies such as the city of Corvallis (Oregon) experiencing persistent cold weather even on a sunny day. We analyze multivariate temperature time-series data and associated covariates from about 160 sensors from the HJ Andrews Research Forest (Oregon) through visualization and modeling to study this phenomenon. We develop detectors to localize cold air pools in both time and space, and carry out simulation studies to assess their performance under different microclimatic and sensor-performance conditions. Keywords: Polynomial regression, Cold Air Pools, Piecewise Linear Regression, Machine Learning
License	All rights reserved
Resource Type	Masters Thesis
Date Available	2017-08-23T20:14:10+00:00
Date Issued	2017-08-01
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2018
Advisor	Wong, Weng-Keen
Committee Member	Hutchinson, Rebecca Raich, Raviv Ramsey, Stephen
Academic Affiliation	Electrical Engineering and Computer Science
Non-Academic Affiliation	Oregon State University. Graduate School
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/61773

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