Genetic algorithm optimized LSTM modeling for dynamic water level regulation in smart garden rainwater recycling systems

Genetic algorithm optimized LSTM modeling for dynamic water level regulation in smart garden rainwater recycling systems | 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 Genetic algorithm optimized LSTM modeling for dynamic water level regulation in smart garden rainwater recycling systems An Guo, Li Jiang, Xiangning Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8675940/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 addresses the dynamic regulation needs of smart garden rainwater recycling systems by proposing a water level predicion model based on a Genetic Algorithm-optimized Long Short-Term Memory network (GA-LSTM). Taking a garden in a southern Chinese city as an example, a bivariate time-series dataset was constructed by integrating precipitation data from meteorological stations and water level sensor data. The genetic algorithm was used to optimize hyperparameters of the LSTM, such as time step and the number of hidden layers. Experimental results show that the GA-LSTM model achieved a root mean square error (RMSE) of 0.143 and a coefficient of determination (R²) of 0.946, demonstrating significant optimization compared to other models. Through the synergistic effect of the rainwater recycling system and the water level prediction model, this study realized dynamic water storage and replenishment regulation for urban garden landscapes. Smart garden Rainwater recycling system LSTM model Water level prediction 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-8675940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587404396,"identity":"ec096d7d-f868-4db3-9fb1-5afa6b3bd4d1","order_by":0,"name":"An Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGklEQVRIie3Pv2qDQBzA8Z9YzHLF9YqQvMKFgqXUkldRhHNRmik4OsWhLV0v0IfII/zCDV1MsgaypAScMuiWqa0mHTqoWTvcd7p/H+4OQKX6hzEE0I9GPXRhUX45/WqkYT1r674mcCaapAa/reedZPSH6EgM6SWXiP2xzC2YyMFdGuyQEh680SfEIuZgps+Nzs4C2ypWcvieHRiOqRPNhA8LkYVAs+W8kSA3LG261QQNASnj0Xzjg7yexsBo1EjYOj+RkaDVw4grA3aRbM63eIK6DAlK95eEHSTXH2D17QtyYPIm4cPZy55Vf+Gk7S9szbXtccIfRS/Yl2XiDMye97krYr9vpq+NpOqKNi6TluN1etGxqVKpVCqAHwJPb0brHfMpAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei Vocational University of Technology and Engineering","correspondingAuthor":true,"prefix":"","firstName":"An","middleName":"","lastName":"Guo","suffix":""},{"id":587404397,"identity":"67b37605-2caa-4c07-b536-cfe3485e1ac0","order_by":1,"name":"Li Jiang","email":"","orcid":"","institution":"Hebei Vocational University of Technology and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Jiang","suffix":""},{"id":587404398,"identity":"365c790e-ce81-4ff9-a3bc-5d58807ed63c","order_by":2,"name":"Xiangning Wang","email":"","orcid":"","institution":"Hebei Vocational University of Technology and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Xiangning","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-23 06:55:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8675940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8675940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397941,"identity":"ec3afdf1-e6be-423b-a40b-145643149040","added_by":"auto","created_at":"2026-03-11 11:58:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":806509,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8675940/v1_covered_9eb5273b-2659-4623-bd90-e8af73d9a95f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic algorithm optimized LSTM modeling for dynamic water level regulation in smart garden rainwater recycling systems","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":"Smart garden, Rainwater recycling system, LSTM model, Water level prediction","lastPublishedDoi":"10.21203/rs.3.rs-8675940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8675940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study addresses the dynamic regulation needs of smart garden rainwater recycling systems by proposing a water level predicion model based on a Genetic Algorithm-optimized Long Short-Term Memory network (GA-LSTM). 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