Author Type

Graduate Student

Date of Award

2024

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Teegavarapu, Ramesh S. V.

Abstract

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.

Extent

265 p.

Comments

Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014414

Format

pdf

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