Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH 513 Pasighat to Yingkiong Arunachal Pradesh India

Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH 513 Pasighat to Yingkiong Arunachal Pradesh India | 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 Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH 513 Pasighat to Yingkiong Arunachal Pradesh India Mrinaljyoti Adhyapok¹, Mrinal Kumar Dutta² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9007331/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract mapping of landslide susceptibility (LSM) along mountain highways in tectonically active regions demands methodologies capable of capturing complex non-linear relationships among geoenvironmental conditioning factors. The present study presents a rigorous comparative investigation of three frameworks—a machine learning-based artificial neural network model (ANN), random‑forest model (RF). In addition, and the conventional AHP multi‑criteria framework (AHP)—for delineating landslide-prone zones along National Highway 513 (NH-513) from Pasighat to Yingkiong (123 km), Arunachal Pradesh, India. Twelve geoenvironmental causative factors were integrated in a GIS environment. A georeferenced landslide inventory of 230 events was partitioned into 70% training and 30% testing subsets. Spatial autocorrelation between training and testing samples was assessed and mitigated through a five-fold spatial block cross-validation (SBCV) scheme applied during hyperparameter tuning. From field observations, with the conventional random split used for final benchmark evaluation alongside a discussion of potential AUC inflation. The ANN model—a multilayer perceptron (MLP) with two fully connected hidden layers (64 and 32 neurons), ReLU activation. Notably, Adam optimiser, and L2 regularisation—achieved a predictive rate AUC of 0.954, substantially outperforming AHP (AUC = 0.551) and demonstrating competitive performance with RF (AUC = 0.980; p = 0.087). A learning-curve analysis confirmed that model performance stabilises near the full training-sample count, providing evidence of sample adequacy. Prediction uncertainty was quantified via bootstrap resampling (n = 100), revealing higher uncertainty in transitional moderate-susceptibility zones. Feature importance analysis consistently identified slope gradient as the dominant conditioning factor and aspect as the least influential. mapping of landslide susceptibility Machine Learning artificial neural network model random‑forest model AHP multi‑criteria framework GIS Arunachal Pradesh Spatial Cross-Validation ROC-AUC NH-513 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 17 Mar, 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. 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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-9007331","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633696950,"identity":"f718fa58-3cd7-4c7f-8457-e849c8e72454","order_by":0,"name":"Mrinaljyoti Adhyapok¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYJACaTDJw3wASErIkKKFLQGkhYcULTwGYIqgct0G5oe3C9vs8vh5znx+daPGgoeB/fDRDfi0mB1gM7ae2ZZcLNnbu8065xjQYTxpaTfwa2Ewk+ZtY07ccJ53m3EOG1CLBI8ZAS3s34Ba6oFaeJ4Z5/wjSgsPyJbDiRvO9jA/zm0jRsthnmJrnnPHiyV7jpkx5/ZJ8LAR9Mvx9o23ecqqgSGW/Phzzrc6OX72w8fwamFgBmJGNoYEIMUmARJgw6scDv6AtTB/IE71KBgFo2AUjDQAAF1FQ3X4nm79AAAAAElFTkSuQmCC","orcid":"","institution":"Jorhat Engineering College, Assam Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Mrinaljyoti","middleName":"","lastName":"Adhyapok¹","suffix":""},{"id":633696951,"identity":"ade6eef4-151a-4a2d-b916-ba051824d511","order_by":1,"name":"Mrinal Kumar Dutta²","email":"","orcid":"","institution":"Jorhat Engineering College, Assam Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Mrinal","middleName":"Kumar","lastName":"Dutta²","suffix":""}],"badges":[],"createdAt":"2026-03-02 08:08:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9007331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9007331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492709,"identity":"ae7c4c0e-37ce-43ad-9afb-95195d895424","added_by":"auto","created_at":"2026-05-05 09:58:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1593353,"visible":true,"origin":"","legend":"","description":"","filename":"lsmahpfinalrevisedfinal1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9007331/v1_covered_2a81bbd9-46a3-43da-8230-d96393fb76f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH 513 Pasighat to Yingkiong Arunachal Pradesh India","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":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mapping of landslide susceptibility, Machine Learning, artificial neural network model, random‑forest model, AHP multi‑criteria framework, GIS, Arunachal Pradesh, Spatial Cross-Validation, ROC-AUC, NH-513","lastPublishedDoi":"10.21203/rs.3.rs-9007331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9007331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003emapping of landslide susceptibility (LSM) along mountain highways in tectonically active regions demands methodologies capable of capturing complex non-linear relationships among geoenvironmental conditioning factors. The present study presents a rigorous comparative investigation of three frameworks\u0026mdash;a machine learning-based artificial neural network model (ANN), random‑forest model (RF). In addition, and the conventional AHP multi‑criteria framework (AHP)\u0026mdash;for delineating landslide-prone zones along National Highway 513 (NH-513) from Pasighat to Yingkiong (123 km), Arunachal Pradesh, India. Twelve geoenvironmental causative factors were integrated in a GIS environment. A georeferenced landslide inventory of 230 events was partitioned into 70% training and 30% testing subsets. Spatial autocorrelation between training and testing samples was assessed and mitigated through a five-fold spatial block cross-validation (SBCV) scheme applied during hyperparameter tuning. From field observations, with the conventional random split used for final benchmark evaluation alongside a discussion of potential AUC inflation. 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