The Som Kamla Amba Catchment Sub-watershed’s Artificial Neural Networks (ANN) and Water Assessment Tool (SWAT) Modelling

The Som Kamla Amba Catchment Sub-watershed’s Artificial Neural Networks (ANN) and Water Assessment Tool (SWAT) Modelling | 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 The Som Kamla Amba Catchment Sub-watershed’s Artificial Neural Networks (ANN) and Water Assessment Tool (SWAT) Modelling Ravi Ande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4432663/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 hydraulic structures, soil conservation structures, water harvesting structures, and flood mitigation studies, and other related projects, rainfall-runoff modelling for unregulated river basins or catchments is useful. The Som Kamla Amba catchment area, which is located in the Rajasthani districts of Dungarpur and Udaipur, is the subject of the current study. Standard statistical indices like r, R2, RMSE, MAE, IA, VE, and NSE were used to evaluate the performance of monthly stream flow and rainfall forecasts created with the Soil and Water Assessment Tool (SWAT) and Artificial Neural Networks (ANN). For the Som Kamla Amba watershed, 27 years (1995–2021) of input data, including daily rainfall, maximum and minimum temperatures, relative humidity, sun radiation, and wind speed, and stream flow, were gathered. The Som Kamla Ambamba catchment's sub watersheds (W1 to W9) had their morphometric parameters examined using ASTER Dem at a spatial resolution of 30 m × 30 m in ArcGIS 10.4.1 software. It was discovered that the ANN model was more accurate and realistic at predicting rainfall. The SWAT model was then discovered to be accurate in forecasting stream flow. Hence, for catchments, basins, or watersheds with comparable hydrological characteristics, ANN and SWAT can be used for rainfall forecasting and stream modelling. drainage systems hydraulic structures Artificial Neural Networks (ANN) Water Assessment Tool (SWAT) Catchment Sub-watershed 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-4432663","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304560104,"identity":"29621666-5da6-4717-b439-5a4ca490950a","order_by":0,"name":"Ravi Ande","email":"data:image/png;base64,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","orcid":"","institution":"University of Delhi","correspondingAuthor":true,"prefix":"","firstName":"Ravi","middleName":"","lastName":"Ande","suffix":""}],"badges":[],"createdAt":"2024-05-16 17:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4432663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4432663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60783753,"identity":"bb238364-0b45-4cb1-9900-8e1c66697144","added_by":"auto","created_at":"2024-07-22 04:16:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1564354,"visible":true,"origin":"","legend":"","description":"","filename":"TheSomKamlaAmbaCatchmentSubwatersheds.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4432663/v1_covered_2c2ed1cf-99f5-4f81-a3a4-b0ed19144f4c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Som Kamla Amba Catchment Sub-watershed’s Artificial Neural Networks (ANN) and Water Assessment Tool (SWAT) Modelling","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":"drainage systems, hydraulic structures, Artificial Neural Networks (ANN), Water Assessment Tool (SWAT), Catchment Sub-watershed","lastPublishedDoi":"10.21203/rs.3.rs-4432663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4432663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hydraulic structures, soil conservation structures, water harvesting structures, and flood mitigation studies, and other related projects, rainfall-runoff modelling for unregulated river basins or catchments is useful. The Som Kamla Amba catchment area, which is located in the Rajasthani districts of Dungarpur and Udaipur, is the subject of the current study. Standard statistical indices like r, R2, RMSE, MAE, IA, VE, and NSE were used to evaluate the performance of monthly stream flow and rainfall forecasts created with the Soil and Water Assessment Tool (SWAT) and Artificial Neural Networks (ANN). For the Som Kamla Amba watershed, 27 years (1995\u0026ndash;2021) of input data, including daily rainfall, maximum and minimum temperatures, relative humidity, sun radiation, and wind speed, and stream flow, were gathered. The Som Kamla Ambamba catchment's sub watersheds (W1 to W9) had their morphometric parameters examined using ASTER Dem at a spatial resolution of 30 m \u0026times; 30 m in ArcGIS 10.4.1 software. It was discovered that the ANN model was more accurate and realistic at predicting rainfall. The SWAT model was then discovered to be accurate in forecasting stream flow. 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