Quantitative Study on Flow-Driving Factors in the Lower Yellow River Bankfull Discharge Based on MIC-RF

Quantitative Study on Flow-Driving Factors in the Lower Yellow River Bankfull Discharge Based on MIC-RF | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantitative Study on Flow-Driving Factors in the Lower Yellow River Bankfull Discharge Based on MIC-RF Jiabei Li, Dangwei Wang, Ke Ni, Jian Chen, Jianguo Chen, Yuhang Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8318421/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study employs a two-stage Maximum Information Coefficient-Random Forest (MIC-RF) model based on discharge, sediment concentration, sediment transport rate, and sediment grain size data from four hydrological stations in the lower reaches of the Yellow River between 1960 and 2022. The model quantifies the contribution of various factors to the interannual variation in bankfull discharge.Results indicate that peak flood discharge (importance 0.300) and Annual Runoff Volume (0.272) are the dominant factors; Maximum sediment transport rate (0.135), median grain size of suspended sediment (0.126), and sediment Inflow Coefficient (0.122) exert indirect influence by regulating the sediment transport-deposition balance; Bed Sediment Median Grain Size (0.045) has a negligible effect. These findings provide quantitative basis for maintaining main channel flow capacity and optimizing water-sediment management. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Bankfull discharge variation Maximum information content Random forest Lower reaches of the Yellow River Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8318421","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":571752097,"identity":"b20294ca-1761-496f-ac7a-c9429fcd4f4c","order_by":0,"name":"Jiabei Li","email":"","orcid":"","institution":"North China University of Water Resources and Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Jiabei","middleName":"","lastName":"Li","suffix":""},{"id":571752098,"identity":"4c294f08-e0c5-42c4-a0d4-56a90bee2105","order_by":1,"name":"Dangwei Wang","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower 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