Salinity prediction in Qiantang Estuary based on Improved LSTM model

Salinity prediction in Qiantang Estuary based on Improved LSTM model | 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 Salinity prediction in Qiantang Estuary based on Improved LSTM model Rong Zheng, Zhilin Sun, Jiange Jiao, Qianqian Ma, Liqin Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4474379/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 Investigating saltwater intrusion is vital for optimal use of estuarine water resources. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity. However, the high nonlinearity, randomness, and instability of salinity sequences pose challenges for accurate estuarine salinity forecasting. In this paper, a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network was proposed, based on measured data from Cangqian and Qibao stations in the Qiantang Estuary during 2011-2012. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). Additionally, the model was applied to forecast salinity under different runoff conditions, analyzing the sensitivity of salinity to upstream discharge. Experimental result shows that compared to other models (BP, GRU, LSTM, SSA-LSTM), the new proposed ISSA-LSTM model has smaller errors and higher prediction accuracy, with NSE improving over 5% and other metrics (MAP, MAPE, RMSE) improving over 10%. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Ocean sciences Saltwater intrusion Salinity prediction Improved Long Short-Term Memory Qiantang Estuary 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. 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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-4474379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310342836,"identity":"65ab1505-1483-4de8-9856-6183f08bd7b9","order_by":0,"name":"Rong Zheng","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zheng","suffix":""},{"id":310342837,"identity":"44272643-becd-42ec-b316-e18a2f4512ea","order_by":1,"name":"Zhilin Sun","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Zhilin","middleName":"","lastName":"Sun","suffix":""},{"id":310342838,"identity":"c957c630-ec56-485e-823f-958189f987b4","order_by":2,"name":"Jiange Jiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIie3RIQvCQBTA8TcOzjK8+kTRr3BiEQx+FUGYZYLRYBNcmVj9GiKIwTARZpl9Ucslw8SiaPBtWt0WBe8fjhvc747HAHS6342bAMwDHMQfndyEdwBlfhJn0vk8RDiH0/GyKVaE416iptyBKNgS7uvvBINeoz5X3MTgsEQkUnLP0pgG34kEi5dNj2YJ+yuIiQxtyYxJChGq8HgSqYW2Skg7k6DFGRChy/n7FcwgGCpWconUA79Bs/RoKDXYTlOImFlGdPP8dnU/Pl1x2KoKp7s43lPIJz9ZGdLfiTdeJgAYJasR5Tiq0+l0/9cLZRBJA+YgrD8AAAAASUVORK5CYII=","orcid":"","institution":"China Jiliang University","correspondingAuthor":true,"prefix":"","firstName":"Jiange","middleName":"","lastName":"Jiao","suffix":""},{"id":310342839,"identity":"db32ccf2-e5ae-45c8-9391-63650445511b","order_by":3,"name":"Qianqian Ma","email":"","orcid":"","institution":"China Jiliang University","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Ma","suffix":""},{"id":310342840,"identity":"79e68aee-f774-4f20-a6d6-8bf79a6b2be2","order_by":4,"name":"Liqin Zhao","email":"","orcid":"","institution":"China Jiliang University","correspondingAuthor":false,"prefix":"","firstName":"Liqin","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-05-24 21:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4474379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4474379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58754308,"identity":"ad977d21-59ce-480f-b9ac-bd615cbaf54e","added_by":"auto","created_at":"2024-06-20 16:27:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851124,"visible":true,"origin":"","legend":"","description":"","filename":"SalinitypredictioninQiantangEstuarybasedonimprovedLSTMmodel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4474379/v1_covered_b6158aec-f20e-4630-8c8d-20034d6879f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Salinity prediction in Qiantang Estuary based on Improved LSTM model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Saltwater intrusion, Salinity prediction, Improved Long Short-Term Memory, Qiantang Estuary","lastPublishedDoi":"10.21203/rs.3.rs-4474379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4474379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Investigating saltwater intrusion is vital for optimal use of estuarine water resources. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity. However, the high nonlinearity, randomness, and instability of salinity sequences pose challenges for accurate estuarine salinity forecasting. In this paper, a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network was proposed, based on measured data from Cangqian and Qibao stations in the Qiantang Estuary during 2011-2012. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). Additionally, the model was applied to forecast salinity under different runoff conditions, analyzing the sensitivity of salinity to upstream discharge. Experimental result shows that compared to other models (BP, GRU, LSTM, SSA-LSTM), the new proposed ISSA-LSTM model has smaller errors and higher prediction accuracy, with NSE improving over 5% and other metrics (MAP, MAPE, RMSE) improving over 10%. 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