Theses and Dissertations

ORCID

https://orcid.org/0000-0003-0978-1950

Advisor

Parajuli, Prem

Committee Member

Linhoss, Anna C.

Committee Member

Webb, Bret M.

Committee Member

Sparks, Eric

Committee Member

Paz, Joel O.

Date of Degree

5-10-2024

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Biological Engineering

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Agricultural and Biological Engineering

Abstract

The spatial variation of wave climate plays a crucial role in erosion, sediment transport, and the design of management actions in coastal areas. Low energy wave systems occur frequently and over a wide range of geographical areas. There is a lack of studies assessing wave model performance in low-energy environments at a regional scale. Therefore, this research aims to model a low energy wave system using a high-resolution dataset. The specific objectives of this study involves 1) using cluster analysis and extensive field measurements to understand the spatial behavior of ocean waves, 2) develop a physics based model of wind-driven waves using high-resolution measurements, and 3) compare machine learning and physics-based models in simulating wave climates. The findings of this study indicate that clustering can effectively assess the spatial variation of the wave climate in a low energy system, with depth identified as the most important influencing factor. Additionally, the physics-based model showed varying performance across different locations within the study area, accurately simulating wave climates in some locations but not in others. Finally, the machine learning model demonstrated overall acceptable performance and accuracy in simulating wave climates and revealed better agreement with observed data in estimating central tendency compared to the physics-based model. The physics-based model performed more favorably for dispersion metrics. These findings contribute to our understanding of coastal dynamics. By providing insights into the spatial behavior of wave climates in low energy systems and comparing the performance of physics-based model and machine learning model, this research contributes to the development of effective coastal management strategies and enhances our understanding of coastal processes.

Download

Share

COinS