Date of Award:
8-2024
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Soukaina Filali Boubrahimi
Committee
Soukaina Filali Boubrahimi
Committee
Shah Muhammad Hamdi
Committee
Hamid Karimi
Abstract
Understanding and predicting streamflow along river basins is vital for planning future developments and ensuring safety, especially with climate change challenges. Our study focused on forecasting streamflow at Lees Ferry, a key location along the Colorado River in the Upper Colorado River Basin. We employed four machine learning models - Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal Auto-Regressive Integrated Moving Average; and combined historical streamflow data with meteorological factors such as snow water equivalent, temperature, and precipitation. Our analysis spanned 30 years of data from 1991 to 2020.
Our findings revealed that the Random Forest Regression model consistently outperformed others, particularly when integrating all meteorological factors with historical streamflow data. Using a 24-month historical data window, our model successfully predicted 12 months of streamflow with a Root Mean Square Error (RMSE) of 2.25 and an R-squared value of 0.80, demonstrating high accuracy. Furthermore, to assess the generalizability of our model, we tested it at other locations within the basin, including Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan).
By enhancing our understanding of streamflow dynamics and leveraging machine learning techniques, our research aims to provide valuable insights for water resource management and decision-making in the face of climate variability.
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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Thota, Saichand, "Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin" (2024). All Graduate Theses and Dissertations, Fall 2023 to Present. 239.
https://digitalcommons.usu.edu/etd2023/239
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