A Geomorphic Process-Based Multi-Objective Optimization Framework for Near-Natural Landform Reconstruction in Mining Areas

A Geomorphic Process-Based Multi-Objective Optimization Framework for Near-Natural Landform Reconstruction in Mining Areas | 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 Geomorphic Process-Based Multi-Objective Optimization Framework for Near-Natural Landform Reconstruction in Mining Areas Jiayuan Kong, Yusheng Liang, Zhenqi Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8592688/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Landform reconstruction of abandoned mine lands is a pivotal component of ecological restoration, where the central challenge lies in systematically balancing long-term ecological stability with immediate economic costs. Conventional near-natural restoration methods predominantly focus on mimicking static geomorphic parameters derived from reference landscapes. While providing valuable quantitative targets, this approach often overlooks the underlying geomorphic evolution processes and lacks a systematic framework for navigating the inherent trade-offs between conflicting objectives. This study aims to transcend these limitations by proposing a novel paradigm for landform reconstruction based on geomorphic process simulation and multi-objective optimization. The framework is designed to concurrently minimize two competing objectives: (1) the potential for water erosion, represented by the topographic factor (LS-factor) of the Revised Universal Soil Loss Equation (RUSLE), and (2) the engineering cost, represented by the total earthwork volume. The powerful Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to computationally evolve and explore the solution space of potential landform designs. A case study was conducted using a high-resolution Digital Elevation Model (DEM) from a typical abandoned mine site in a loess hilly region. The optimization successfully generated a Pareto optimal front, which explicitly quantifies the trade-off relationship between ecological stability and economic cost. The results demonstrate that the framework can provide decision-makers with a diverse portfolio of optimal design schemes, ranging from ‘cost-priority’ to ‘ecology-priority,’ and quantitatively illustrates the best achievable erosion control levels for different budgetary constraints. Meanwhile, plan subsequent revegetation and management strategies accordingly. Through detailed morphological and statistical analysis of representative schemes, it was verified that the process-driven optimization spontaneously converges towards forming geomorphic features that are remarkably similar to the natural reference area. This research not only provides a scientific and efficient decision-support tool for landform reconstruction but also promotes a paradigm shift in mine ecological restoration from ‘form mimicking’ to ‘process-oriented, intelligent optimization,’ offering a new application of geographical information science to solve complex environmental management problems. Near-Natural Landform Design Mine Reclamation Multi-Objective Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 13 Jan, 2026 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-8592688","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578430877,"identity":"f75b0994-1834-423c-a35b-7df2426b6a92","order_by":0,"name":"Jiayuan Kong","email":"","orcid":"","institution":"China University of Mining and Technology-Beijing","correspondingAuthor":false,"prefix":"","firstName":"Jiayuan","middleName":"","lastName":"Kong","suffix":""},{"id":578430878,"identity":"0bc817bb-8309-408b-a858-2b560a556bc0","order_by":1,"name":"Yusheng Liang","email":"","orcid":"","institution":"Northeastern 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Areas","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"geoscience-letters","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gosl","sideBox":"Learn more about [Geoscience Letters](https://geoscienceletters.springeropen.com/)","snPcode":"40562","submissionUrl":"https://submission.springernature.com/new-submission/40562/3","title":"Geoscience Letters","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Near-Natural Landform Design, Mine Reclamation, Multi-Objective","lastPublishedDoi":"10.21203/rs.3.rs-8592688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8592688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Landform reconstruction of abandoned mine lands is a pivotal component of ecological restoration, where the central challenge lies in systematically balancing long-term ecological stability with immediate economic costs. Conventional near-natural restoration methods predominantly focus on mimicking static geomorphic parameters derived from reference landscapes. While providing valuable quantitative targets, this approach often overlooks the underlying geomorphic evolution processes and lacks a systematic framework for navigating the inherent trade-offs between conflicting objectives. This study aims to transcend these limitations by proposing a novel paradigm for landform reconstruction based on geomorphic process simulation and multi-objective optimization. The framework is designed to concurrently minimize two competing objectives: (1) the potential for water erosion, represented by the topographic factor (LS-factor) of the Revised Universal Soil Loss Equation (RUSLE), and (2) the engineering cost, represented by the total earthwork volume. The powerful Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to computationally evolve and explore the solution space of potential landform designs. A case study was conducted using a high-resolution Digital Elevation Model (DEM) from a typical abandoned mine site in a loess hilly region. The optimization successfully generated a Pareto optimal front, which explicitly quantifies the trade-off relationship between ecological stability and economic cost. The results demonstrate that the framework can provide decision-makers with a diverse portfolio of optimal design schemes, ranging from ‘cost-priority’ to ‘ecology-priority,’ and quantitatively illustrates the best achievable erosion control levels for different budgetary constraints. Meanwhile, plan subsequent revegetation and management strategies accordingly. Through detailed morphological and statistical analysis of representative schemes, it was verified that the process-driven optimization spontaneously converges towards forming geomorphic features that are remarkably similar to the natural reference area. This research not only provides a scientific and efficient decision-support tool for landform reconstruction but also promotes a paradigm shift in mine ecological restoration from ‘form mimicking’ to ‘process-oriented, intelligent optimization,’ offering a new application of geographical information science to solve complex environmental management problems.","manuscriptTitle":"A Geomorphic Process-Based Multi-Objective Optimization Framework for Near-Natural Landform Reconstruction in Mining Areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 11:36:31","doi":"10.21203/rs.3.rs-8592688/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-07T04:52:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146671613826871434014407251020756322639","date":"2026-04-03T12:16:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T01:55:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T05:09:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T02:28:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Geoscience Letters","date":"2026-01-13T13:29:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"geoscience-letters","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gosl","sideBox":"Learn more about [Geoscience Letters](https://geoscienceletters.springeropen.com/)","snPcode":"40562","submissionUrl":"https://submission.springernature.com/new-submission/40562/3","title":"Geoscience Letters","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35668dfe-f7c7-42bd-90d4-cb4474d14005","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T11:36:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 11:36:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8592688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8592688","identity":"rs-8592688","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}