Modeling Freshwater Yield: Deep Learning Applications in Seawater Greenhouses in Iran

Modeling Freshwater Yield: Deep Learning Applications in Seawater Greenhouses in Iran | 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 Modeling Freshwater Yield: Deep Learning Applications in Seawater Greenhouses in Iran Amirhossein Barzigar, Sevda Allahyari, Mohsen Fathi, Arun Sadashiv Mujumdar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6692588/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The seawater greenhouse (SWGH) is an environmentally friendly solution that utilizes solar-driven desalination techniques to produce freshwater while simultaneously creating a controlled agricultural environment. Integrating SWGH with green buildings optimizes sustainability by reducing dependence on conventional water supplies and lowering carbon emissions. This study develops a deep learning-based predictive model to optimize freshwater production in SWGHs, particularly in the Makran region. The Makran coast faces severe freshwater shortages due to its arid climate, limited groundwater resources, and growing agricultural demand. SWGH technology is particularly suitable for this region, leveraging abundant solar radiation and seawater to sustainably generate freshwater while enhancing agricultural productivity and environmental resilience. This study aims to develop a deep learning-based predictive model to forecast freshwater production in SWGHs for integration into green building frameworks. The model forecasts freshwater yield by analyzing environmental, especially climate and operational parameters. A two-stage deep learning-based prediction approach was employed, utilizing CNN-LSTM, BiLSTM, BiGRU, CNN-GRU, and MLP models. First, global horizontal irradiance (GHI) was predicted as a primary factor influencing SWGH performance. Then, freshwater production was estimated using predicted solar radiation. Among tested models, CNN-LSTM achieved the highest accuracy with achieving a R 2 of 0.9727, a RMSE of 0.0021, and a MSE of 0.0022. The freshwater production rate was predicted per unit area, and the average annual yield for 2024–2033 was estimated at 1454.25 L/m². The results confirm SWGH as a viable solution for sustainable water management in arid coastal regions. Physical sciences/Engineering/Mechanical engineering Earth and environmental sciences/Environmental sciences/Environmental impact Physical sciences/Energy science and technology/Renewable energy Freshwater Deep learning Long-term prediction Seawater greenhouse Green building Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 10 Aug, 2025 Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers invited by journal 03 Jun, 2025 Editor invited by journal 28 May, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 24 May, 2025 First submitted to journal 24 May, 2025 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. 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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-6692588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465798199,"identity":"5c86aeb9-f272-4b82-98da-3de1064b867f","order_by":0,"name":"Amirhossein Barzigar","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Amirhossein","middleName":"","lastName":"Barzigar","suffix":""},{"id":465798200,"identity":"b33815c6-c792-459e-9bab-e7199fdfd339","order_by":1,"name":"Sevda Allahyari","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sevda","middleName":"","lastName":"Allahyari","suffix":""},{"id":465798201,"identity":"d61de053-6118-4961-b8a0-bead81b91581","order_by":2,"name":"Mohsen Fathi","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Fathi","suffix":""},{"id":465798202,"identity":"33ff533d-05df-4430-bb36-83bc419f71ae","order_by":3,"name":"Arun Sadashiv Mujumdar","email":"","orcid":"","institution":"McGill University","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"Sadashiv","lastName":"Mujumdar","suffix":""},{"id":465798203,"identity":"08bc2c62-bdec-494e-95b5-b2c0bc784826","order_by":4,"name":"Seyed Mostafa Hosseinalipour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACdsYGECXHBhNgw6kUBpghWoyBKiEsIrRAqMQGmBaCgL+Zue3Bx7Z76X0S6c8fMNTYMfBJH8CvReIwY7vhzLbi3DaJHMMGhmPJDGx8CQSsOczYJs3blgDSAnQY2wEGNh4COuShWtLZJNIfNjD8I0KLAVRLAptEgmEDYxsRWgxBfplxLsGwjeeN4YzEvmQeglrkjrc/e/ChLEFevj39wYcP3+zk5HsIaGFAibsEBgZCdqBrGQWjYBSMglGADQAACBk3VU7UCfQAAAAASUVORK5CYII=","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Seyed","middleName":"Mostafa","lastName":"Hosseinalipour","suffix":""}],"badges":[],"createdAt":"2025-05-18 15:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6692588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6692588/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-20548-y","type":"published","date":"2025-10-21T16:16:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490489,"identity":"d7e6423a-9c65-48b9-b9d8-b05508dd8070","added_by":"auto","created_at":"2025-10-27 17:11:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1430727,"visible":true,"origin":"","legend":"","description":"","filename":"Predictionmainarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6692588/v1_covered_8c4650b4-ca25-4296-95ae-6a1ec0b60236.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling Freshwater Yield: Deep Learning Applications in Seawater Greenhouses in Iran","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Freshwater, Deep learning, Long-term prediction, Seawater greenhouse, Green building","lastPublishedDoi":"10.21203/rs.3.rs-6692588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6692588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe seawater greenhouse (SWGH) is an environmentally friendly solution that utilizes solar-driven desalination techniques to produce freshwater while simultaneously creating a controlled agricultural environment. Integrating SWGH with green buildings optimizes sustainability by reducing dependence on conventional water supplies and lowering carbon emissions. This study develops a deep learning-based predictive model to optimize freshwater production in SWGHs, particularly in the Makran region. The Makran coast faces severe freshwater shortages due to its arid climate, limited groundwater resources, and growing agricultural demand. SWGH technology is particularly suitable for this region, leveraging abundant solar radiation and seawater to sustainably generate freshwater while enhancing agricultural productivity and environmental resilience. This study aims to develop a deep learning-based predictive model to forecast freshwater production in SWGHs for integration into green building frameworks. The model forecasts freshwater yield by analyzing environmental, especially climate and operational parameters. 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