Land Subsidence Monitoring and Forecasting in Mining Areas Based on SBAS-InSAR and SSA-BP Neural Models

Land Subsidence Monitoring and Forecasting in Mining Areas Based on SBAS-InSAR and SSA-BP Neural Models | 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 Land Subsidence Monitoring and Forecasting in Mining Areas Based on SBAS-InSAR and SSA-BP Neural Models Yaqi Liu, Qiuxiang Tao, TengFei Gao, Pengcheng Wang, Ziting Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7129689/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This study investigates ground subsidence in the mining areas of Heze City, Shandong Province, using SBAS-InSAR technology and an SSA-BP neural network model. A total of 54 Sentinel-1A SAR images acquired from February 2019 to January 2024 were processed to derive cumulative subsidence and average subsidence rate maps. The results show that severe subsidence mainly occurs in three key coal mining zones, with a maximum annual rate of 182 mm/year and cumulative subsidence reaching − 878 mm. The subsidence area spans approximately 1129.28 km², accounting for 59.33% of the total study area. To predict future deformation trends, a back propagation neural network optimized using the Sparrow Search Algorithm was constructed. Model performance was evaluated against LSTM, SVR, and traditional BP networks. The SSA-BP model achieved the highest accuracy, with RMSE values as low as 1.89 mm. Comparison with leveling measurements validated the reliability of the SBAS-InSAR results. This study demonstrates the feasibility and accuracy of combining SBAS-InSAR and SSA-BP models for subsidence monitoring and prediction in coal mining areas, offering valuable references for early warning and disaster prevention efforts. SBAS-InSAR SSA-BP models Ground subsidence Mining area Time series prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 16 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 15 Jul, 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. 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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-7129689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508218012,"identity":"132101eb-c7c4-4142-9d08-ef0d806f8773","order_by":0,"name":"Yaqi Liu","email":"","orcid":"","institution":"College of Geodesy and Geomatics, Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yaqi","middleName":"","lastName":"Liu","suffix":""},{"id":508218013,"identity":"80873902-92b9-4628-a40b-83e10391e044","order_by":1,"name":"Qiuxiang Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACZiBOgDAZHyRU1JCghQfINHhw5hgJtgG1sEk+bGEmrJLvOI+ZxIOaO3b72Q8fq0hsYGPgb+9OwKtF8jCPsUHCsWfJPTxpaTcSd8gwSJw5uwGvFoPDPIYPEtgOJ/Mw5JjdSDzDxmAgkUtQi8GBhH9ALfxvzAoS25iJ0mL4ILHtsB2PRI4ZA1FaJA+zFRsk9h1O4LnxLFki4cwxHoJ+4Tt/eJvkj2+H7dn7kw9+/FFRI8ff3otfC8MBCJXYAOXz4FeOpMWesMpRMApGwSgYsQAAarhJpKrcYRUAAAAASUVORK5CYII=","orcid":"","institution":"College of Geodesy and Geomatics, Shandong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Qiuxiang","middleName":"","lastName":"Tao","suffix":""},{"id":508218015,"identity":"b54166bb-b077-4cb7-8d15-11cdb94f0bb2","order_by":2,"name":"TengFei Gao","email":"","orcid":"","institution":"Shandong GEO-Surveying \u0026 Mapping Institute","correspondingAuthor":false,"prefix":"","firstName":"TengFei","middleName":"","lastName":"Gao","suffix":""},{"id":508218017,"identity":"b97378af-0f02-4e09-aef4-baa87bbc0f1e","order_by":3,"name":"Pengcheng Wang","email":"","orcid":"","institution":"College of Geodesy and Geomatics, Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Pengcheng","middleName":"","lastName":"Wang","suffix":""},{"id":508218018,"identity":"1e16a7c8-0fe7-41c6-98d3-c7c5ddc4e96a","order_by":4,"name":"Ziting Zhao","email":"","orcid":"","institution":"College of Geodesy and Geomatics, Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ziting","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-07-15 10:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7129689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7129689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90501661,"identity":"c1c4a3aa-fc17-41eb-8c7c-5c8a64654fe3","added_by":"auto","created_at":"2025-09-03 11:50:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1374441,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscripts.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7129689/v1_covered_75063fbb-c7a7-43f3-9f4c-4696eb7ba6da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land Subsidence Monitoring and Forecasting in Mining Areas Based on SBAS-InSAR and SSA-BP Neural Models","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":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SBAS-InSAR, SSA-BP models, Ground subsidence, Mining area, Time series prediction","lastPublishedDoi":"10.21203/rs.3.rs-7129689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7129689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates ground subsidence in the mining areas of Heze City, Shandong Province, using SBAS-InSAR technology and an SSA-BP neural network model. A total of 54 Sentinel-1A SAR images acquired from February 2019 to January 2024 were processed to derive cumulative subsidence and average subsidence rate maps. The results show that severe subsidence mainly occurs in three key coal mining zones, with a maximum annual rate of 182 mm/year and cumulative subsidence reaching \u0026minus;\u0026thinsp;878 mm. The subsidence area spans approximately 1129.28 km\u0026sup2;, accounting for 59.33% of the total study area. To predict future deformation trends, a back propagation neural network optimized using the Sparrow Search Algorithm was constructed. Model performance was evaluated against LSTM, SVR, and traditional BP networks. The SSA-BP model achieved the highest accuracy, with RMSE values as low as 1.89 mm. Comparison with leveling measurements validated the reliability of the SBAS-InSAR results. This study demonstrates the feasibility and accuracy of combining SBAS-InSAR and SSA-BP models for subsidence monitoring and prediction in coal mining areas, offering valuable references for early warning and disaster prevention efforts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Land Subsidence Monitoring and Forecasting in Mining Areas Based on SBAS-InSAR and SSA-BP Neural Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 11:18:14","doi":"10.21203/rs.3.rs-7129689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-12T17:11:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T08:34:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208833415428131459080985566987272334339","date":"2025-09-01T03:30:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-26T21:15:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T04:38:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T04:36:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2025-07-15T10:48:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0a3c52af-ff40-473c-baf8-e1926a1cf13a","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-07T10:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 11:18:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7129689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7129689","identity":"rs-7129689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}