Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Long Short-Term Memory for Karst Watershed Modeling: Case Study of Logan River Canyon, UT, USA

Description
Hydrologic modeling in snowfed karst watersheds is important for many communities relying on their water for municipal and agricultural use, but the complexities of karst hydrology have made this task historically difficult. Here, two Long Short-Term Memory (LSTM) models are compared to investigate this problem from a deep-learning perspective within…

Hydrologic modeling in snowfed karst watersheds is important for many communities relying on their water for municipal and agricultural use, but the complexities of karst hydrology have made this task historically difficult. Here, two Long Short-Term Memory (LSTM) models are compared to investigate this problem from a deep-learning perspective within the context of the Logan River Canyon watershed, which supplies water to Logan City, UT. One is spatially lumped and the other spatially distributed, the latter with a potential to reveal underlying spatial watershed dynamics. Both use snowmelt and rainfall to predict daily streamflow downstream. I find distributed LSTMs consistently outperform lumped LSTMs in this task. Additionally, I find that a spatial sensitivity analysis of distributed LSTMs is unpromising in revealing spatial watershed dynamics but warrants further investigation.
ContributorsShaver, Ryan (Author) / Xu, Tianfang (Thesis director) / Jones, Don (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05