Ensemble Runoff Forecasting Based on Multiple Machine Learning Methods

Ensemble Runoff Forecasting Based on Multiple Machine Learning Methods | 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 Research Article Ensemble Runoff Forecasting Based on Multiple Machine Learning Methods Xinyu Wan, Xinyu Wang, Kun Lyu, Yixuan Li, Fangzheng Zhao, Yuting Xue, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5414792/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 The accuracy of runoff forecasting is influenced by factors, such as the type of prediction model and parameter settings. To account for these uncertainties and leverage the strengths of different runoff forecasting models, in this study, we developed an ensemble forecasting model. It assigned combination weights to each model based on its prediction accuracy, improving the accuracy of short-term runoff forecasts. Using daily runoff data from the Dongqiaoyuan Hydrological Station in the Rongjiang River Basin, China, six runoff forecasting models were established by coupling ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) with a Backpropagation Neural Network, convolutional neural network, and support vector machine. Two ensemble methods, namely, the optimal weighting method and stacking algorithm, were applied to the EMD- and EEMD-based coupled models, respectively, and their predictive performances were comparatively analyzed. Different data preprocessing methods significantly affected the performances of both the individual and ensemble models. The ensemble models had a considerable reduction in the root mean square error and mean absolute percentage error. The random forest stacking ensemble model showed the highest improvement and prediction accuracy. The model greatly enhances the prediction accuracy of large flows in the Rongjiang River Basin, meeting the needs for short-term forecasting. It can effectively support runoff prediction during extreme environmental changes in the river basin. Additionally, it serves as a valuable reference for enhancing ensemble prediction outcomes in runoff forecasting. Daily Runoff Coupled model Ensemble Machine Learning Runoff Forecasting Full Text 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. 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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-5414792","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377597245,"identity":"843bd0dd-413e-413b-854f-4695b1e73258","order_by":0,"name":"Xinyu 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