Document Type
Article
Author ORCID Identifier
Saichand Thota https://orcid.org/0009-0007-8452-1039
Ayman Nassar https://orcid.org/0000-0003-0878-5861
Soukaina Filali Boubrahimi https://orcid.org/0000-0001-5693-6383
Shah Muhammad Hamdi https://orcid.org/0000-0002-9303-7835
Pouya Hosseinzadeh https://orcid.org/0000-0001-8045-2709
Journal/Book Title/Conference
Hydrology
Volume
11
Issue
5
Publisher
MDPI AG
Publication Date
5-1-2024
Journal Article Version
Version of Record
First Page
1
Last Page
30
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Meat Square Error of 2.25 and R-squared (R2) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin.
Recommended Citation
Thota, S.; Nassar, A.; Filali Boubrahimi, S.; Hamdi, S.M.; Hosseinzadeh, P. Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin. Hydrology 2024, 11, 66. https://doi.org/10.3390/hydrology11050066