Journal Article

The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning

Pierce, Jennifer L., Chang, Shuyu Y., Ghahremani, Zahra, Van Meter, Kimberly J., Erfani, Seyed Mohammad Hassan, Goharian, Erfan, Cohen, Sagy, Shen, Chaopeng, Meselhe, Ehab A., Manuel, Laura

2024

Overview

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. Plain Language Summary Scientists and river managers use measurements of river geometry such as width and depth to forecast floods and understand river behavior. However, the methods used to estimate river geometry that have been used for decades are imprecise and thus lead to poor predictions of river discharge dynamics. Here, we've used new machine learning‐based modeling approaches to provide better predictions of river width and depth. We tested different machine‐learning models, which were developed based on the HYDRoSWOT set of measurements of rivers across the U.S. These new models all provide better estimates of river width and depth than the old methods. Our research can help us to provide better estimates of flood dynamics and improve our understanding of rivers across the U.S. Key Points Machine Learning models outperform regional (physiographic) hydraulic geometry equations for predicting stream width and depth Model performance varies by stream orders and geographical regions, demonstrating the utility of multi‐model machine‐learning approaches The STREAM‐geo data set provides predictions of river width, depth, width‐to‐depth ratio, and river area for the NHDPlus stream reaches

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: artificial intelligence / Creeks & streams / Curves / data collection / Datasets / Depth / Estimates / Flood forecasting / Flood management / Flood predictions / Flooding / Floods / Geometry / Hydraulic geometry / Hydraulics / Hydrogeomorphology / hydrology / Learning algorithms / Machine learning / Neural networks / Predictions / Regional development / riparian areas / River channels / river depth / River discharge / River flow / river geometry / river geomorphology / river width / Rivers / sediments / streams / surface area / water / Water depth / Water discharge / watersheds / Width