Date of Award
2012
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Abstract
This thesis develops methodologies for continuous estimation of hydrological variables which infill missing daily rainfall data and the forecast of weekly streamflows from a watershed. Several mathematical programming formulations were developed and used to estimate missing historical rainfall data. Functional relationships were created between radar precipitation and known rain gauge data then are used to estimate the missing data. Streamflow predictions models require highly non-linear mathematical models to capture the complex physical characteristics of a watershed. An artificial neural network model was developed for streamflow prediction. There are no set methods of creating a neural network and the selection of architecture and inputs to a neural network affects the performance. This thesis addresses this issue with automated input and network architecture selection through optimization. MATLABª scripts are developed and used to test many combinations and select a model through optimization.
Extent
xiv, 134 p. : ill. (some col.)
Recommended Citation
Brown, Ricardo Eric, "Optimization and inductive models for continuous estimation of hydrologic variables" (2012). Electronic Theses and Dissertations 2010 - 2019. 600.
https://digitalcommons.fau.edu/etd_general_6/600
Identifier
FADT3342036
Comments
by Ricardo Eric Brown.|Thesis (M.S.C.S.)--Florida Atlantic University, 2012.|Includes bibliography.|Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.|by Ricardo Eric Brown.