Dissertation/Thesis Abstract

Optimization and inductive models for continuous estimation of hydrologic variables
by Brown, Ricardo Eric, M.S., Florida Atlantic University, 2012, 149; 1517812
Abstract (Summary)

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. MATLAB® scripts are developed and used to test many combinations and select a model through optimization.

Indexing (document details)
Advisor: Teegavarapu, Ramesh S. V.
School: Florida Atlantic University
Department: Civil, Environmental and Geomatics Engineering
School Location: United States -- Florida
Source: MAI 51/01M(E), Masters Abstracts International
Subjects: Hydrologic sciences, Civil engineering, Water Resource Management
Publication Number: 1517812
ISBN: 978-1-267-48109-2
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