Spatial and temporal interpolation methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these interpolation methods have not been comprehensively checked for their ability to preserve time series characteristics. Assessing the preservation of time series characteristics helps achieving a threshold criteria of length of gaps in a data set that is acceptable to be filled. This study evaluates the efficacy of optimal weighting interpolation for estimation of missing data in preserving time series characteristics. Rain gauges in the state of Kentucky are used as a case study. Several model performance measures are also evaluated to validate the filling model; followed by time series characteristics to evaluate the accuracy of estimation and preservation of precipitation data characteristics. This study resulted in a definition of region-specific threshold of the maximum length of gaps allowed in a data set at five percent. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection
Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_33922 |
Contributors | Hachmi, Mohammad (author), Teegavarapu, Ramesh (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering |
Publisher | Florida Atlantic University |
Source Sets | Florida Atlantic University |
Language | English |
Detected Language | English |
Type | Electronic Thesis or Dissertation, Text |
Format | 124 p., application/pdf |
Rights | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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