Deep Learning Models for Small Rivers Stream-Flow Forecasting and Flood Prediction

Deep Learning Models for Small Rivers Stream-Flow Forecasting and Flood Prediction | 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 Deep Learning Models for Small Rivers Stream-Flow Forecasting and Flood Prediction Mohammed Albared, Hans-Peter Beise, Manfred Stüber This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4689483/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 River streamflow forecasting is essential for water resources management and flood damage mitigation. Due to their superiority at solving time series problems, several studies have evaluated deep learning models for river streamflow forecasting. However, forecasting small river flow rates, especially during flood events, is a challenge of particular difficulty due to the scarcity of data and the sudden appearance of floods. This work investigates the performance of five deep-learning models for small river streamflow forecasting and particularly focuses on flow forecasting during flood events. The models considered in this work are long short-term memory (LSTM), Gated Recurrent units (GRU), Bidirectional LSTM, 1D convolutional neural networks (1DCNN), and sequential ConvLSTM models. In addition to the standard performance metrics used to evaluate models, this work introduces and applies a new evaluation metric to measure the time effectiveness of models in the early prediction of floods, taking into account the time budget to take early actions. The models are used to forecast the flow rate values over the next 6 hours, 3 hours, and 1 hour. Kyll River, a small river in western Germany, was chosen as a case study because it was subject to several flood events. Results show that all models achieved good performance in overall small river flow forecasting. Results also show that the LSTM-based and the GRU models exhibited poor streamflow forecasting performance (accuracy and time) during normal and extreme flood periods and showed a big decrease in their performance during extreme floods. The findings of this study show that 1DCNN had the highest streamflow forecasting performance, and it reaches impressive forecast accuracy on the entire test set periods and even in normal and extreme flood periods. This suggests that the 1DCNN algorithm can be used as a part of an early flood warning and prediction system in case of both normal and extreme floods. Deep Learning Stream-Flow Rate Forecasting Extreme Flood 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-4689483","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338588569,"identity":"7653cfd6-bd34-4af8-8895-b4a1a69ddfac","order_by":0,"name":"Mohammed Albared","email":"data:image/png;base64,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","orcid":"","institution":"Trier University of Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Albared","suffix":""},{"id":338588570,"identity":"9ecad970-85cb-499c-9566-88eb2664567a","order_by":1,"name":"Hans-Peter Beise","email":"","orcid":"","institution":"Trier University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hans-Peter","middleName":"","lastName":"Beise","suffix":""},{"id":338588571,"identity":"9cd2f13c-ee22-4439-ba01-f7bce265d643","order_by":2,"name":"Manfred Stüber","email":"","orcid":"","institution":"Trier University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Manfred","middleName":"","lastName":"Stüber","suffix":""}],"badges":[],"createdAt":"2024-07-05 04:06:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4689483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4689483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62401656,"identity":"38a42cc8-3788-40fe-b00c-25307b81e18a","added_by":"auto","created_at":"2024-08-13 19:10:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1485325,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4689483/v1_covered_7ac1bfc3-f7c7-4b16-b5ae-514cf4ab31d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning Models for Small Rivers Stream-Flow Forecasting and Flood Prediction","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":"Deep Learning, Stream-Flow Rate, Forecasting, Extreme Flood Prediction","lastPublishedDoi":"10.21203/rs.3.rs-4689483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4689483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiver streamflow forecasting is essential for water resources management and flood damage mitigation. Due to their superiority at solving time series problems, several studies have evaluated deep learning models for river streamflow forecasting. However, forecasting small river flow rates, especially during flood events, is a challenge of particular difficulty due to the scarcity of data and the sudden appearance of floods. This work investigates the performance of five deep-learning models for small river streamflow forecasting and particularly focuses on flow forecasting during flood events. The models considered in this work are long short-term memory (LSTM), Gated Recurrent units (GRU), Bidirectional LSTM, 1D convolutional neural networks (1DCNN), and sequential ConvLSTM models. In addition to the standard performance metrics used to evaluate models, this work introduces and applies a new evaluation metric to measure the time effectiveness of models in the early prediction of floods, taking into account the time budget to take early actions. The models are used to forecast the flow rate values over the next 6 hours, 3 hours, and 1 hour. Kyll River, a small river in western Germany, was chosen as a case study because it was subject to several flood events. Results show that all models achieved good performance in overall small river flow forecasting. Results also show that the LSTM-based and the GRU models exhibited poor streamflow forecasting performance (accuracy and time) during normal and extreme flood periods and showed a big decrease in their performance during extreme floods. The findings of this study show that 1DCNN had the highest streamflow forecasting performance, and it reaches impressive forecast accuracy on the entire test set periods and even in normal and extreme flood periods. This suggests that the 1DCNN algorithm can be used as a part of an early flood warning and prediction system in case of both normal and extreme floods.\u003c/p\u003e","manuscriptTitle":"Deep Learning Models for Small Rivers Stream-Flow Forecasting and Flood Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 18:54:42","doi":"10.21203/rs.3.rs-4689483/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"23e55d7a-9f02-4d68-ba19-3cb0dafc41d2","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T18:54:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-13 18:54:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4689483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4689483","identity":"rs-4689483","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}