[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124862-en":3,"doc-seo-124862-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124862,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","The Geometry of Flow - Advancing Predictions of River Geometry with Multi-Model Machine Learning","Hydraulic geometry parameters describing river hydrogeomorphic structure are critical for reliable flood forecasting. While power-law hydraulic geometry curves have long supported river analysis and inundation mapping worldwide, their limitations are increasingly recognized. This study evaluates machine-learning models to improve predictions of river width and depth using the large HYDRoSWOT measurement dataset and watershed predictor variables for the contiguous United States (CONUS). Random Forest, XGBoost, and neural networks outperform traditional regional power-law equations and motivate multi-model strategies to maximize predictability.","The geometry of flow: Advancing predictions of river geometry with multimodel machine learning  \nAuthors: Shuyu Y Chang 1,2 , Zahra Ghahremani3 , Laura Manuel4 , Mohammad Erfani5 , Chaopeng Shen6 , Sagy Cohen7 , Kimberly Van Meter 1,2 , Jennifer L Pierce3 , Ehab A Meselhe4 , Erfan Goharian5  \n1Department of Geography, Pennsylvania State University, 302 Walker Building, University Park, PA 16803  \n2Earth and Environmental Systems Institute, 2217 Earth-Engineering Sciences Building University Park, PA 16802-6813  \n3Department of Geoscience, Boise State University, Environmental Research Building 1160, Boise, ID 83725  \n4Department of River-Coastal Science and Engineering, Tulane University, 6823 St. Charles Avenue New Orleans, LA 70118  \n5Department of Civil and Environmental Engineering, Univerity of South Carolina, 300 Main St, Room C206, Columbia, SC 29208  \n6Department of Civil and Environmental Engineering Engineering, Pennsylvania State University, 231CSackett Bulding, University Park, PA 16803  \n7Department of Geography, University of Alabama, Shelby Hall 2019-E, Tuscaloosa, AL, 35487  \nCorresponding authors’ Email: [cshen@engr.psu.edu](cshen@engr.psu.edu)  \n[sagy.cohen@ua.edu](sagy.cohen@ua.edu)  \n[vanmeterkvm@psu.edu](vanmeterkvm@psu.edu)  \nAbstract  \nHydraulic geometry parameters describing river hydrogeomorphic 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 the limitations of these approaches. In the present study, we have moved beyond these traditional power-law relationships for river geometry, 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 dataset (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 ofas high as 0.75 for width and as high as 0.67 for depth, compared with Rsquared values of 0.57 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 dataset, 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 rivers and streams across the contiguous US.  \nPlain Language Summary  \nScientists 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.  \nMain Points  \n1. Machine Learning models outperform regional (physiographic) hydraulic geometry equations for predicting stream width and depth.  \n2. Model performance varies by stream ","cbCaifkPbgPLCXnv","https://ap.wps.com/l/cbCaifkPbgPLCXnv","pdf",2565846,1,53,"English","en",105,"# Abstract\n# Plain Language Summary\n# Main Points\n# Introduction","[{\"question\":\"Why are hydraulic geometry parameters important for flood forecasting?\",\"answer\":\"Hydraulic geometry parameters such as river width and depth describe hydrogeomorphic river structure, which is needed to predict flood dynamics and river discharge behavior.\"},{\"question\":\"What problem do traditional power-law hydraulic geometry curves have?\",\"answer\":\"Power-law relationships have been used for decades, but their limitations can lead to imprecise estimates and weaker flood predictions.\"},{\"question\":\"How do the proposed machine-learning models improve river geometry predictions?\",\"answer\":\"The study develops Random Forest, XGBoost, and neural network models trained on HYDRoSWOT measurements and watershed predictors, and these models outperform regionalized power-law equations for both width and depth.\"}]","The Geometry of Flow - Advancing Predictions of River Geometry with Multi-Model Machine Learning | PDF",1785895094,134,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-geometry-of-flow-advancing-predictions-of-river-geometry-with-multi-model-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-geometry-of-flow-advancing-predictions-of-river-geometry-with-multi-model-machine-learning/124862/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are hydraulic geometry parameters important for flood forecasting?","Question",{"text":75,"@type":76},"Hydraulic geometry parameters such as river width and depth describe hydrogeomorphic river structure, which is needed to predict flood dynamics and river discharge behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem do traditional power-law hydraulic geometry curves have?",{"text":80,"@type":76},"Power-law relationships have been used for decades, but their limitations can lead to imprecise estimates and weaker flood predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed machine-learning models improve river geometry predictions?",{"text":84,"@type":76},"The study develops Random Forest, XGBoost, and neural network models trained on HYDRoSWOT measurements and watershed predictors, and these models outperform regionalized power-law equations for both width and depth.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]