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ESTIMATING MISSING PRECIPITATION RECORDS USING TREE-BASED APPROACHES

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Date Issued:
2024
Abstract/Description:
Missing rainfall records happens frequently in many areas, and making precipitation estimation has been a challenge due to the spatial-temporal variability of the parameter. Model tree (MT), regression tree (RT), and ensemble approach models were developed and evaluated for estimating missing precipitation values in this research study. The selection of stations using correlation coefficient and similar distribution, and variation of data used to build the model were applied in this study. Proposed models were developed and tested using daily rainfall data from 1971 to 2016 at twenty-two stations in Kentucky, U.S.A. The model results were analyzed and evaluated using error and performance measures. The results indicated that MT-based and ensemble models produce a better estimation of missing rainfall than regression trees. The MT-based model was able to estimate missing rainfall accurately without needing objective selection of stations and using minimal calibration data to build the model.
Title: ESTIMATING MISSING PRECIPITATION RECORDS USING TREE-BASED APPROACHES.
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Name(s): Nguyen, Thu , author
Teegavarapu, Ramesh S. V. , Thesis advisor
Florida Atlantic University, Degree grantor
Department of Civil, Environmental and Geomatics Engineering
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2024
Date Issued: 2024
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 265 p.
Language(s): English
Abstract/Description: Missing rainfall records happens frequently in many areas, and making precipitation estimation has been a challenge due to the spatial-temporal variability of the parameter. Model tree (MT), regression tree (RT), and ensemble approach models were developed and evaluated for estimating missing precipitation values in this research study. The selection of stations using correlation coefficient and similar distribution, and variation of data used to build the model were applied in this study. Proposed models were developed and tested using daily rainfall data from 1971 to 2016 at twenty-two stations in Kentucky, U.S.A. The model results were analyzed and evaluated using error and performance measures. The results indicated that MT-based and ensemble models produce a better estimation of missing rainfall than regression trees. The MT-based model was able to estimate missing rainfall accurately without needing objective selection of stations and using minimal calibration data to build the model.
Identifier: FA00014414 (IID)
Degree granted: Thesis (MS)--Florida Atlantic University, 2024.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Precipitation (Meteorology)
Estimating techniques
Missing data (Statistics)
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014414
Use and Reproduction: 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.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU