Doctoral Dissertations

Keywords and Phrases

Data Visualization; Energy; Strategic Planning; Sustainability; Time Series; Transportation

Abstract

“Current infrastructure systems modeling literature lacks frameworks that integrate data visualization and trend extraction needed for complex systems decision making and planning. Critical infrastructures such as transportation and energy systems contain interdependencies that cannot be properly characterized without considering data visualization and trend extraction.

This dissertation presents two case analyses to showcase the effectiveness and improvements that can be made using these techniques. Case one examines flood management and mitigation of disruption impacts using geospatial characteristics as part of data visualization. Case two incorporates trend analysis and sustainability assessment into energy portfolio transitions.

Four distinct contributions are made in this work and divided equally across the two cases. The first contribution identifies trends and flood characteristics that must be included as part of model development. The second contribution uses trend extraction to create a traffic management data visualization system based on the flood influencing factors identified. The third contribution creates a data visualization framework for energy portfolio analysis using a genetic algorithm and fuzzy logic. The fourth contribution develops a sustainability assessment model using trend extraction and time series forecasting of state-level electricity generation in a proposed transition setting.

The data visualization and trend extraction tools developed and validated in this research will improve strategic infrastructure planning effectiveness”--Abstract, page iv.

Advisor(s)

Long, Suzanna, 1961-

Committee Member(s)

Qin, Ruwen
Canfield, Casey I.
Crow, Mariesa

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Comments

The authors gratefully acknowledge partial support for this project through funding provided by the Missouri Department of Transportation, TR201912, and the Mid-America Transportation Center, 25-1121-0005-130.

The authors gratefully acknowledge the DOE SUNSHOT GEARED program for partially funding this research through DOE Project DE-EE0006341.

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2021

Journal article titles appearing in thesis/dissertation

  • Flood Management Deep Learning Model Inputs: A Review of Necessary Data and Predictive Tools
  • Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control
  • A Computational Intelligence Approach to Transitioning an Electricity Portfolio
  • A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Pagination

xii, 102 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2021 Jacob Marshal Hale, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11896

Electronic OCLC #

1286686959

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