Effects of stacking LSTM with different patterns and input schemes on streamflow and water quality simulation

Effects of stacking LSTM with different patterns and input schemes on streamflow and water quality simulation | 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 Effects of stacking LSTM with different patterns and input schemes on streamflow and water quality simulation Yucong Hu, Yan Jiang, Huiting Yao, Yiping Chen, Xuefeng Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3740192/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract Streamflow and water quality parameters (WQs) are commonly forecasted by mechanism models and statistics models. However, these models are challenged due to computational complexity, redundant parameters, etc. Therefore, a stacking Long short-term memory networks (LSTM) model with two patterns and different input schemes was applied to simulate streamflow and eight WQs in this study. The results showed that sliding windows was detected as the more stable pattern for both forecasts. The accuracy of predicting streamflow using only meteorological inputs was limited especially with low-volume flow. Whereas, the prediction of WQs with three input variables (i.e., meteorological factors, streamflow, other influential WQs) was reliable reaching an average relative error (RE) below 17%. When adding historical data into the input dataset, both accuracies could be increased close to benchmarks of the Delft 3D model. Our study documents that the LSTM model is an effective method for streamflow and water quality forecasts. Long short-term memory (LSTM) Streamflow forecast Water quality forecast pattern input schemes Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revision 07 Feb, 2024 Reviewers agreed at journal 19 Jan, 2024 Reviewers invited by journal 18 Jan, 2024 Editor invited by journal 08 Jan, 2024 Editor assigned by journal 18 Dec, 2023 First submitted to journal 10 Dec, 2023 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. 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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-3740192","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268114297,"identity":"43f98b30-00e2-4cf0-bd52-cd80b1c6c032","order_by":0,"name":"Yucong Hu","email":"","orcid":"","institution":"Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yucong","middleName":"","lastName":"Hu","suffix":""},{"id":268114298,"identity":"cdb4bcfb-a99a-4f3b-9fd3-5caa65e5eff1","order_by":1,"name":"Yan Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJACgwQgwU+6FskG0q06QLTK42cPFDyouWO3+fbhhw8+/GGQ529gfvYAr5YzeQkGCceeJW87l2ZsOLONwXDGATZzA/zuyTEwSGA7nGx2hodNmreBgXEDAw+bBF4t598Atfw7nGzcw8P++88fBnvCWm4AbUlsO2xnwMPDxszAxpBIUIvkDaAtiX2HEyTOsBlL9rZJJM84zGaGVwvf+Rwzwx/fDtvz9zA//PDjj41tf3vzM7xaFA4wsIHCJ7EBwgcqZsanHgjkGxiYHwBpewLqRsEoGAWjYCQDAMMtR+beXAIqAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Jiang","suffix":""},{"id":268114299,"identity":"2f80868d-baa5-4ba5-9ce3-286114fcb01e","order_by":2,"name":"Huiting Yao","email":"","orcid":"","institution":"Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Huiting","middleName":"","lastName":"Yao","suffix":""},{"id":268114300,"identity":"7a608d84-6de2-4a7f-a29c-54b57e3d3b32","order_by":3,"name":"Yiping Chen","email":"","orcid":"","institution":"Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Chen","suffix":""},{"id":268114301,"identity":"1a4ae3a6-c6bf-4ba8-ad9f-671592b80951","order_by":4,"name":"Xuefeng Wu","email":"","orcid":"","institution":"Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2023-12-11 19:02:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3740192/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3740192/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50017304,"identity":"c5e66c7f-4636-4b6d-9299-fcd46cc3b235","added_by":"auto","created_at":"2024-01-23 07:04:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1149786,"visible":true,"origin":"","legend":"","description":"","filename":"LSTMESPR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3740192/v1_covered_19a32514-5787-4c59-b340-f4b3c04f546c.pdf"}],"financialInterests":"","formattedTitle":"Effects of stacking LSTM with different patterns and input schemes on streamflow and water quality simulation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Long short-term memory (LSTM), Streamflow forecast, Water quality forecast, pattern, input schemes","lastPublishedDoi":"10.21203/rs.3.rs-3740192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3740192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStreamflow and water quality parameters (WQs) are commonly forecasted by mechanism models and statistics models. 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