Hydrogeological Characteristics of the Namdock Coal Mine Area and an Evaluation Method for Water Inflow Prediction Using the Entropy Weight Method and ANFIS (Adaptive Neuro-Fuzzy Inference System)

Hydrogeological Characteristics of the Namdock Coal Mine Area and an Evaluation Method for Water Inflow Prediction Using the Entropy Weight Method and ANFIS (Adaptive Neuro-Fuzzy Inference System) | 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 Hydrogeological Characteristics of the Namdock Coal Mine Area and an Evaluation Method for Water Inflow Prediction Using the Entropy Weight Method and ANFIS (Adaptive Neuro-Fuzzy Inference System) Jae-Myong Li, Kum-Hyok Choe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8742775/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 Water inrush is a critical geological hazard restricting safe and efficient mining operations in the Namdock Coal Mine, which is situated in a complex hydrogeological setting with developed faults, fractured aquifers, and variable lithological compositions. This study systematically characterizes the hydrogeological features of the mine area and proposes a novel water inflow prediction method integrating the Entropy Weight Method (EWM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). First, field discharge observations and drilling surveys were conducted to clarify the mine’s hydrogeological structure, including water-bearing strata distribution, fault-conductive pathways, and seasonal water inflow variability. Then, the EWM was applied to quantify discharge risk levels for different mining drifts, identifying high-risk zones with entropy weight values exceeding 0.2221. On this basis, an ANFIS model was established using six key influencing factors (mining depth, coal seam thickness, dip angle, hanging wall failure degree, geological structure, and season) as inputs and measured water inflow as output. The model was trained with 25 groups of field data and validated with 5 groups of test data, achieving a low test error of 1.0158%—significantly outperforming the traditional BP neural network (8.56% test error). Field application in the mine’s 9-Pit area demonstrated that the integrated method could accurately predict water inflow in high-risk drifts and guide the optimization of mining sequences. This research provides a scientific and efficient technical tool for water inrush prevention in anthracite coal mines with complex hydrogeological conditions. Geology Namdock Coal Mine Hydrogeological Characteristics Water Inflow Prediction Entropy Weight Method ANFIS Full Text Additional Declarations The authors declare no competing interests. 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-8742775","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583157090,"identity":"f2ac66f0-c723-4e99-8779-28880cb36f69","order_by":0,"name":"Jae-Myong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACPoYDjI//VNTIoYgyNuDRwsZwgNmA58wxYyALBpgJaWFgE+BtY05sIF4L4+k0Bsk2tvT585uffWCosWEwZ+8/wDhzBz5bzm57YHBOJnfDMTbjGQzH0hgsew4zMG48g1fLdoOEMrbcDWwMxsx/Gw4zGNxIZmB82IbfFokDbMzp8m3sn4F+AGq5/5iwFsmGNuYEhmM8xhAtN4De34hfy2ZjhjPHDDccyylmAPqFx+BMssHBmXj8wi9xduNjhooaefnm45sZgCEmZ3D84MOHvXhCjEHiACqfB0QcwBsx/Fgl8WoZBaNgFIyCkQYA3e5QdL6ugrgAAAAASUVORK5CYII=","orcid":"","institution":"School of Geoscience and Technology, Kim Chaek University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jae-Myong","middleName":"","lastName":"Li","suffix":""},{"id":583157091,"identity":"8809083f-91ff-4c4f-8b81-089a54275730","order_by":1,"name":"Kum-Hyok Choe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFCCBMbHPxgSEviZmQ8/IFYLszGQSJBsZ0szIFYLmzRIi8F5HgUJojTws+cYSBfUpOUZH+ZhMGCosYkmqEWy542B8YxjOcVmh3kPPGA4lpbbQEiLwY0cgwTehorEbYf5EgwYGw4T1mIP1HIApGVzM4+BBFFaDCRyDJt5G3ISNzATq0XizLNixhnH0hJnHAYGcgIxfuFvT97+40NNcmJ//+HDDz7U2BDWggoSSFM+CkbBKBgFowAXAAAC1EDxsbB55QAAAABJRU5ErkJggg==","orcid":"","institution":"School of Geoscience and Technology, Kim Chaek University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Kum-Hyok","middleName":"","lastName":"Choe","suffix":""}],"badges":[],"createdAt":"2026-01-30 15:28:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8742775/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8742775/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101754423,"identity":"6b4a49f9-dc1b-4d50-bbcc-bf46238299f0","added_by":"auto","created_at":"2026-02-03 10:42:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1015176,"visible":true,"origin":"","legend":"","description":"","filename":"file.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8742775/v1_covered_19187e51-e4de-4f2d-bc73-d0befebe65dc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHydrogeological Characteristics of the Namdock Coal Mine Area and an Evaluation Method for Water Inflow Prediction Using the Entropy Weight Method and ANFIS (Adaptive Neuro-Fuzzy Inference System)\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kim Chaek University of Technology","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":"Namdock Coal Mine, Hydrogeological Characteristics, Water Inflow Prediction, Entropy Weight Method, ANFIS","lastPublishedDoi":"10.21203/rs.3.rs-8742775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8742775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater inrush is a critical geological hazard restricting safe and efficient mining operations in the Namdock Coal Mine, which is situated in a complex hydrogeological setting with developed faults, fractured aquifers, and variable lithological compositions. This study systematically characterizes the hydrogeological features of the mine area and proposes a novel water inflow prediction method integrating the Entropy Weight Method (EWM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). First, field discharge observations and drilling surveys were conducted to clarify the mine\u0026rsquo;s hydrogeological structure, including water-bearing strata distribution, fault-conductive pathways, and seasonal water inflow variability. Then, the EWM was applied to quantify discharge risk levels for different mining drifts, identifying high-risk zones with entropy weight values exceeding 0.2221. On this basis, an ANFIS model was established using six key influencing factors (mining depth, coal seam thickness, dip angle, hanging wall failure degree, geological structure, and season) as inputs and measured water inflow as output. The model was trained with 25 groups of field data and validated with 5 groups of test data, achieving a low test error of 1.0158%\u0026mdash;significantly outperforming the traditional BP neural network (8.56% test error). Field application in the mine\u0026rsquo;s 9-Pit area demonstrated that the integrated method could accurately predict water inflow in high-risk drifts and guide the optimization of mining sequences. 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