The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning
Abstract Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036733 |
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| author | Shuyu Y. Chang Zahra Ghahremani Laura Manuel Seyed Mohammad Hassan Erfani Chaopeng Shen Sagy Cohen Kimberly J. Van Meter Jennifer L. Pierce Ehab A. Meselhe Erfan Goharian |
| author_facet | Shuyu Y. Chang Zahra Ghahremani Laura Manuel Seyed Mohammad Hassan Erfani Chaopeng Shen Sagy Cohen Kimberly J. Van Meter Jennifer L. Pierce Ehab A. Meselhe Erfan Goharian |
| author_sort | Shuyu Y. Chang |
| collection | DOAJ |
| description | Abstract Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US. |
| format | Article |
| id | doaj-art-c69bb7dcabb24327b8deb648d9bea80a |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-c69bb7dcabb24327b8deb648d9bea80a2025-08-20T03:30:57ZengWileyWater Resources Research0043-13971944-79732024-10-016010n/an/a10.1029/2023WR036733The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine LearningShuyu Y. Chang0Zahra Ghahremani1Laura Manuel2Seyed Mohammad Hassan Erfani3Chaopeng Shen4Sagy Cohen5Kimberly J. Van Meter6Jennifer L. Pierce7Ehab A. Meselhe8Erfan Goharian9Department of Geography Pennsylvania State University University Park PA USADepartment of Geoscience Boise State University Boise ID USADepartment of River‐Coastal Science and Engineering Tulane University New Orleans LA USADepartment of Civil and Environmental Engineering University of South Carolina Columbia SC USADepartment of Civil and Environmental Engineering Pennsylvania State University University Park PA USADepartment of Geography University of Alabama Tuscaloosa AL USADepartment of Geography Pennsylvania State University University Park PA USADepartment of Geoscience Boise State University Boise ID USADepartment of River‐Coastal Science and Engineering Tulane University New Orleans LA USADepartment of Civil and Environmental Engineering University of South Carolina Columbia SC USAAbstract Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US.https://doi.org/10.1029/2023WR036733river geomorphologyriver geometryriver widthriver depthhydrologymachine learning |
| spellingShingle | Shuyu Y. Chang Zahra Ghahremani Laura Manuel Seyed Mohammad Hassan Erfani Chaopeng Shen Sagy Cohen Kimberly J. Van Meter Jennifer L. Pierce Ehab A. Meselhe Erfan Goharian The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning Water Resources Research river geomorphology river geometry river width river depth hydrology machine learning |
| title | The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning |
| title_full | The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning |
| title_fullStr | The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning |
| title_full_unstemmed | The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning |
| title_short | The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning |
| title_sort | geometry of flow advancing predictions of river geometry with multi model machine learning |
| topic | river geomorphology river geometry river width river depth hydrology machine learning |
| url | https://doi.org/10.1029/2023WR036733 |
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