Graduate Studies

 

First Advisor

Tirthankar Roy

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

David Admiraal, Jongwan Eun, Milad Roohi

Department

Civil Engineering (Water Resources)

Date of this Version

5-2025

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Doctor of Philosophy

Major: Civil Engineering (Water Resources)

Under the supervision of Professor Tirthankar Roy

Lincoln, Nebraska, May 2025

Comments

Copyright 2025, Sudan Pokharel. Used by permission

Abstract

Due to climate change and its impact, the need for adaptive strategies for natural disaster mitigation and resource management has never been more urgent. Central to this is water resource management, which is essential for sustainable human activities, ecological balance, and the mitigation of natural hazards like floods. Streamflow is a crucial element of water resource management and plays a vital role in planning and building water infrastructure, implementing emergency response plans, supporting flood mitigation initiatives, and regulating agricultural and industrial use. However, accurate prediction of streamflow still remains a challenge due to the complex non-linear and non-stationary interaction between hydrological and climate processes and human action. These challenges highlight the need for innovative and reliable prediction methods. Due to advancements in technology, data availability, and computer power, Machine Learning (ML) is showing promise in this field. However, there are still some gaps, such as including physics in ML models, utilizing the power of spatial and temporal signals, and data synergy. This dissertation fills these gaps by developing robust ML frameworks that enhance both scientific understanding and practical implementation for streamflow prediction.

Advisor: Tirthankar Roy

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