A novel hybrid machine learning approach for suspended sediment load forecasting: A case study of Mazandaran rivers basins

A novel hybrid machine learning approach for suspended sediment load forecasting: A case study of Mazandaran rivers basins | 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 A novel hybrid machine learning approach for suspended sediment load forecasting: A case study of Mazandaran rivers basins Ali Akbar Eatesam, Khosrow Hosseini, Hojat Karami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7607699/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Estimating the suspended sediment load (SSL) in watersheds is a fundamental challenge in surface water resource management and hydraulic structure design. This study utilized data from the Haraz, Babol Rud, Talar, and Neka Rud watersheds in Mazandaran province, Iran, encompassing discharge, daily sediment load, and key physical characteristics. These data were organized in three different combinations as inputs to several machine learning models. Among these, the input scenario M 3 , which combines streamflow data, sediment load, and the physical characteristics of the basins, demonstrated the best performance across all models. The machine learning models employed were Support Vector Regression (SVR), Long-Short Term Memory (LSTM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) the performance of which was enhanced using two optimization algorithms: Particle Swarm Optimization (PSO), and Flow Direction Algorithm (FDA). While both optimization methods produced acceptable results, the FDA algorithm demonstrated superior performance. The RF-FDA hybrid model achieved the highest accuracy, yielding the following statistical metrics during the training phase: RMSE = 2.45, MAE = 1.98, R = 0.95, KGE = 0.87, and NSE = 0.90. The corresponding metrics in the testing phase were 3.26, 2.44, 0.92, 0.88, and 0.84, respectively. The findings of this study have significant implications for engineers and policymakers in the design of hydraulic structures and water resources management. Suspended sediment load Machine learning models Optimization algorithms Flow Direction Algorithm (FDA) Prediction Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 30 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 24 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 13 Sep, 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. 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-7607699","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524734928,"identity":"99c6f392-d0c7-44c6-9df0-7ea85b2c87e7","order_by":0,"name":"Ali Akbar Eatesam","email":"","orcid":"","institution":"Semnan University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Akbar","lastName":"Eatesam","suffix":""},{"id":524734929,"identity":"14efe192-9c99-40ef-a5fd-20ad13c14116","order_by":1,"name":"Khosrow 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This study utilized data from the Haraz, Babol Rud, Talar, and Neka Rud watersheds in Mazandaran province, Iran, encompassing discharge, daily sediment load, and key physical characteristics. These data were organized in three different combinations as inputs to several machine learning models. Among these, the input scenario M\u003csub\u003e3\u003c/sub\u003e, which combines streamflow data, sediment load, and the physical characteristics of the basins, demonstrated the best performance across all models. The machine learning models employed were Support Vector Regression (SVR), Long-Short Term Memory (LSTM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) the performance of which was enhanced using two optimization algorithms: Particle Swarm Optimization (PSO), and Flow Direction Algorithm (FDA). While both optimization methods produced acceptable results, the FDA algorithm demonstrated superior performance. The RF-FDA hybrid model achieved the highest accuracy, yielding the following statistical metrics during the training phase: RMSE\u0026thinsp;=\u0026thinsp;2.45, MAE\u0026thinsp;=\u0026thinsp;1.98, R\u0026thinsp;=\u0026thinsp;0.95, KGE\u0026thinsp;=\u0026thinsp;0.87, and NSE\u0026thinsp;=\u0026thinsp;0.90. The corresponding metrics in the testing phase were 3.26, 2.44, 0.92, 0.88, and 0.84, respectively. The findings of this study have significant implications for engineers and policymakers in the design of hydraulic structures and water resources management.\u003c/p\u003e","manuscriptTitle":"A novel hybrid machine learning approach for suspended sediment load forecasting: A case study of Mazandaran rivers basins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 14:37:06","doi":"10.21203/rs.3.rs-7607699/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T18:29:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T09:10:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T10:12:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T06:41:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T23:24:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282781983531224878273847464144726723661","date":"2025-09-27T12:07:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T19:05:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85667656022648580632382692145415663063","date":"2025-09-25T05:33:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156620813354070037759357429732964352368","date":"2025-09-25T00:06:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213635242293353605612348355519453942018","date":"2025-09-24T21:08:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170857870298300965156200713877053654400","date":"2025-09-24T21:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300742288207211808245992672968706560314","date":"2025-09-24T20:41:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T18:44:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T06:57:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T06:56:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Modeling Earth Systems and Environment","date":"2025-09-13T13:26:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"modeling-earth-systems-and-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mese","sideBox":"Learn more about [Modeling Earth Systems and Environment](http://link.springer.com/journal/40808)","snPcode":"40808","submissionUrl":"https://submission.springernature.com/new-submission/40808/3","title":"Modeling Earth Systems and Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a95fd2ff-3afe-4b8d-be0f-75e057af66dc","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T19:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 14:37:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7607699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7607699","identity":"rs-7607699","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}