Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines

Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines | 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 Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines Arun Kumar Sahoo, Debi Prasad Tripathy, Singam Jayanthu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7050792/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The majority of India's coal is mined using opencast methods, which causes more waste dumps to be formed and stability problems. There is a higher chance of dump instability because to the 1,148 million cubic meters of overburden that Coal India Limited (CIL) has removed in the past few years. Complex calculations make dump slope stability studies complicated and time-consuming. Analytical and numerical methods are needed to calculate factor of safety (FOS) of dump slope. This research bridges traditional geotechnical methods with emerging computational approaches by integrating advanced ML techniques with rigorous statistical evaluation and a comprehensive dataset to improve dump slope stability prediction accuracy, reliability, and applicability. With so many available options, picking the best ML model can be a challenge. Consequently, for the purpose of this research, the authors selected models using the Lazy predict AutoML algorithm. Using six base models—Gradient Boosting (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGB), Nu-Support Vector Regressor (Nu-SVR), Extra Tree Regressor (ETR), with Stacking Ensemble, and H2OAutoML—this study proposes an effective method for analysing dump slope stability. In preparation for model calibration and evaluation, databases of 2250 datasets were created. The output is the factor of safety (FOS) and the inputs are six influential parameters such as cohesion (c), angle of internal friction (ϕ), unit weight (γ), overall bench height (H), natural moisture content (m), and overall slope angle (β). The coefficient of determination (R squared or R 2 ), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error(MAE) were used for evaluating the performance of all models. The H2O Auto ML performed best model in comparison to other ensemble models. This research also makes use of the Shapley additive explanations (SHAP) technique to determine which of the six inputs is most crucial. This study shows that sophisticated ML approaches improve dump slope stability prediction in Indian opencast coal mines. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Solid earth sciences Dump slope stability prediction Machine learning models Lazy predicts H2O Auto ML and Stacking Ensemble Model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor invited by journal 10 Jul, 2025 Editor assigned by journal 08 Jul, 2025 Submission checks completed at journal 06 Jul, 2025 First submitted to journal 05 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7050792","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484197246,"identity":"87753940-867b-469b-8e2c-938d80ad083a","order_by":0,"name":"Arun Kumar Sahoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYFACHhS6hocfRCUUEK/lmIxkA0iLAfFamG0MDoBoPFrk288efPCh5l7idp4zpht+7mHjMT6/OvHDAwMGeX6xA1i1GJzJSzaccaw4cWdvj9nNnmcyPGY33m6WADrMcObsBOxaGHLMpHnYEhI3nAcq5jnABiTPbgBpSTC4jV2LfP8boJZ/EC03/xxg5jGecXbzD3xaGG4AbeFtA2o522N2mweoxYC/dxteWwxuvDE2nNmXYLzhzLGy2zIHjvFI3ODdZpFgIIHTL/L9OYYPPnxLkN1wJnnbzTcHauz5+89uvvmjwkaeXxqHwzCBBFilBLHKQYD/ACmqR8EoGAWjYAQAABKuY2aYfh77AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Arun","middleName":"Kumar","lastName":"Sahoo","suffix":""},{"id":484197247,"identity":"0d047d7c-0fce-4096-ab23-c018c6b40535","order_by":1,"name":"Debi Prasad Tripathy","email":"","orcid":"","institution":"National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Debi","middleName":"Prasad","lastName":"Tripathy","suffix":""},{"id":484197248,"identity":"a6d5515d-329a-4537-879b-dfe67da53733","order_by":2,"name":"Singam Jayanthu","email":"","orcid":"","institution":"National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Singam","middleName":"","lastName":"Jayanthu","suffix":""}],"badges":[],"createdAt":"2025-07-05 06:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7050792/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7050792/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24680-7","type":"published","date":"2025-11-20T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96650336,"identity":"a79072b1-cdbb-44a1-9e85-7130acc17314","added_by":"auto","created_at":"2025-11-24 16:11:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2066098,"visible":true,"origin":"","legend":"","description":"","filename":"Applicationofadvancedmachinelearningtechniques.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7050792/v1_covered_8dda5ae0-0779-4bd0-b219-e2c9fce7f1e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dump slope stability prediction, Machine learning models, Lazy predicts, H2O Auto ML and Stacking Ensemble Model","lastPublishedDoi":"10.21203/rs.3.rs-7050792/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7050792/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe majority of India's coal is mined using opencast methods, which causes more waste dumps to be formed and stability problems. There is a higher chance of dump instability because to the 1,148\u0026nbsp;million cubic meters of overburden that Coal India Limited (CIL) has removed in the past few years. Complex calculations make dump slope stability studies complicated and time-consuming. Analytical and numerical methods are needed to calculate factor of safety (FOS) of dump slope. This research bridges traditional geotechnical methods with emerging computational approaches by integrating advanced ML techniques with rigorous statistical evaluation and a comprehensive dataset to improve dump slope stability prediction accuracy, reliability, and applicability. With so many available options, picking the best ML model can be a challenge. Consequently, for the purpose of this research, the authors selected models using the Lazy predict AutoML algorithm. Using six base models\u0026mdash;Gradient Boosting (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGB), Nu-Support Vector Regressor (Nu-SVR), Extra Tree Regressor (ETR), with Stacking Ensemble, and H2OAutoML\u0026mdash;this study proposes an effective method for analysing dump slope stability. In preparation for model calibration and evaluation, databases of 2250 datasets were created. The output is the factor of safety (FOS) and the inputs are six influential parameters such as cohesion (c), angle of internal friction (ϕ), unit weight (γ), overall bench height (H), natural moisture content (m), and overall slope angle (β). The coefficient of determination (R squared or R\u003csup\u003e2\u003c/sup\u003e), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error(MAE) were used for evaluating the performance of all models. The H2O Auto ML performed best model in comparison to other ensemble models. This research also makes use of the Shapley additive explanations (SHAP) technique to determine which of the six inputs is most crucial. This study shows that sophisticated ML approaches improve dump slope stability prediction in Indian opencast coal mines.\u003c/p\u003e","manuscriptTitle":"Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 05:34:20","doi":"10.21203/rs.3.rs-7050792/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-28T06:14:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T12:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329066257200807173770018028423870453193","date":"2025-07-26T02:28:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T08:11:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209306391700224210426296322029135729587","date":"2025-07-22T11:23:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34305597600362011288758577726880400910","date":"2025-07-16T02:36:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T13:38:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-10T12:57:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T05:32:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T02:39:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-05T05:53:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd210796-c120-4a08-8a18-45c2cf1ffe87","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51414425,"name":"Physical sciences/Energy science and technology"},{"id":51414426,"name":"Physical sciences/Engineering"},{"id":51414427,"name":"Earth and environmental sciences/Environmental sciences"},{"id":51414428,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2025-11-24T16:06:31+00:00","versionOfRecord":{"articleIdentity":"rs-7050792","link":"https://doi.org/10.1038/s41598-025-24680-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-20 15:58:18","publishedOnDateReadable":"November 20th, 2025"},"versionCreatedAt":"2025-07-14 05:34:20","video":"","vorDoi":"10.1038/s41598-025-24680-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24680-7","workflowStages":[]},"version":"v1","identity":"rs-7050792","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7050792","identity":"rs-7050792","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}