Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network

Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network | 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 Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network Gaolin Liu, Guangjin Wang, Wenlian Liu, Bing Zhao, Rong Lan, Bisheng Wu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3833912/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 Tailing reservoir is an important auxiliary facility of mine and a dangerous source of man-made debris flow with high potential energy. China’s tailings ponds are shifting toward fine-grained high dams. Accordingly, displacement is one of the key factors affecting pond stability, and it is important to understand the displacement trend of the tailings pond to ensure its safe operation. Accordingly, this paper adopts the whale algorithm to optimize the back propagation(BP) neural network and establishes the WOA-BP neural network nonlinear prediction model to avoid the error generated by the model experiment due to the scaling effect. The infiltration line and displacement data of a tailings pond in Sichuan Province in the past two years are collected consecutively to form a learning sample, which is then used for training to predict the displacement of the tailings pond through the WOA-BP neural network model. Thereafter, these prediction results are compared with the actual monitoring values as well as the BP neural network model prediction values. The results revealed that the relative error of the WOA-BP neural network model prediction results was approximately 4.5%, and the Pearson correlation coefficients were all above 0.998. Compared with the traditional BP neural network model, the optimization model has a stronger search capability, wider application range, higher prediction accuracy, a more global optimal solution, and better response. The nonlinear fuzzy mapping provides new insights into tailings pond displacement and safety prediction. Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Energy infrastructure fine-grained tailings pond infiltration line displacement whale optimization algorithm BP neural network 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-3833912","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268336541,"identity":"b0f432e6-25d7-4dfa-bb45-7f4c2df494a8","order_by":0,"name":"Gaolin Liu","email":"","orcid":"","institution":"Faculty of Land Resources Engineering, Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Gaolin","middleName":"","lastName":"Liu","suffix":""},{"id":268336542,"identity":"4ed50f1e-d22a-42c8-9173-98b8a898925d","order_by":1,"name":"Guangjin 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China\u0026rsquo;s tailings ponds are shifting toward fine-grained high dams. Accordingly, displacement is one of the key factors affecting pond stability, and it is important to understand the displacement trend of the tailings pond to ensure its safe operation. Accordingly, this paper adopts the whale algorithm to optimize the back propagation(BP) neural network and establishes the WOA-BP neural network nonlinear prediction model to avoid the error generated by the model experiment due to the scaling effect. The infiltration line and displacement data of a tailings pond in Sichuan Province in the past two years are collected consecutively to form a learning sample, which is then used for training to predict the displacement of the tailings pond through the WOA-BP neural network model. Thereafter, these prediction results are compared with the actual monitoring values as well as the BP neural network model prediction values. The results revealed that the relative error of the WOA-BP neural network model prediction results was approximately 4.5%, and the Pearson correlation coefficients were all above 0.998. Compared with the traditional BP neural network model, the optimization model has a stronger search capability, wider application range, higher prediction accuracy, a more global optimal solution, and better response. The nonlinear fuzzy mapping provides new insights into tailings pond displacement and safety prediction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":" Displacement prediction of fine-grained tailings ponds based on WOA-BP neural network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 08:25:58","doi":"10.21203/rs.3.rs-3833912/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":"de68a9fb-2421-4494-aac7-8e3438074b77","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28266918,"name":"Physical sciences/Engineering/Civil engineering"},{"id":28266919,"name":"Physical sciences/Engineering/Energy infrastructure"}],"tags":[],"updatedAt":"2024-07-08T10:54:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 08:25:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3833912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3833912","identity":"rs-3833912","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}