Doctoral Dissertations

Keywords and Phrases

Clustering; Deep Learning; Flood Prediction; Machine Learning; Sensor Placement; Water Level Prediction

Abstract

Floods represent formidable natural calamities, posing a significant threat to communities and infrastructure due to their unpredictable and often devastating consequences. The occurrence of floods is influenced by a convergence of meteorological, hydrological, and geographical factors, resulting in changes to the patterns of rising water levels. Machine learning models have emerged as favored tools in recent times for modeling water levels and enhancing the precision of flood predictions. This research employs both supervised and unsupervised machine learning models, with the main objective of improving the accuracy of flood predictions and sensor placement. Four distinct deep learning models are used to forecast water levels for a designated set of gage stations in Missouri. The comparative analysis assesses the effectiveness of four deep learning models, considering various prediction intervals and parameter tuning to bolster result robustness. Subsequently, the study incorporates an unsupervised learning algorithm to identify optimal locations for placement of new water level sensors. Unlike conventional sensor placement algorithms, this strategy focuses on dynamic water level patterns to extract important features that are indicative of informative gage locations. The methodology is evaluated using a deep learning model to ascertain its efficacy. The research concludes by pinpointing locations for water level sensors in Missouri. The result of these studies enables a thorough examination of accuracy-cost trade-offs before deciding to new sensors and providing efficient ways to analyze the Missouri river networks with less computational intensity.

Advisor(s)

Corns, Steven

Committee Member(s)

Dagli, Cihan H., 1949-
Wunsch, Donald C.
Nicolosi, Gabriel
Holmes, Robert R., 1965-

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2025

Journal article titles appearing in thesis/dissertation

Paper I, found on pages 16–50, has been published in MDPI Water,
Paper II, found on pages 51–69, has been published in the proceedings of the international Annual Conference of the American Society for Engineering Management.
Paper III, found on pages 70–115, is intended for submission to Journal of Environmental Management.
Paper IV, found on pages 116–142, is intended for submission to Journal of Hydrology.

Pagination

xii, 149 pages

Note about bibliography

Includes_bibliographical_references_(pages 146-147)

Rights

© 2025 Fahimeh Sharafkhani , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12519

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