Optimization of land subsidence prediction features based on machine learning and SHAP value with Sentinel-1 InSAR Data

Optimization of land subsidence prediction features based on machine learning and SHAP value with Sentinel-1 InSAR Data | 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 Optimization of land subsidence prediction features based on machine learning and SHAP value with Sentinel-1 InSAR Data Heng Su, Tingting Xu, Xiancai Xion, Aohua Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3880879/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 Land subsidence has always been a concern of geoscience, and exploring the factors affecting land subsidence to predict future land subsidence is essential research. However, current research rarely has a scientific and unified feature screening process for land subsidence features. This study applies neural networks and SHAP values to land subsidence prediction. We used SHAP values instead of the traditional random forest (RF) to quantify land subsidence features and neural networks to predict the areas where land subsidence is likely to occur in the cities of Chongqing and Chengdu, encompassing the majority of the possible land subsidence scenarios in the future. The results show that the prediction of land subsidence using neural networks improves the model accuracy by 16% compared to the traditional method. After input features optimization, the performance improves by nearly 22%. We found that the feature optimization method based on SHAP values proposed in this study is more helpful for land subsidence prediction, and the factors affecting land subsidence derived from data analysis with complex terrain are also consistent with the results of previous studies. This feature optimization method can contribute to the input variable selection process for the land subsidence prediction model, improve accuracy, and provide solid theoretical support for preventing urban land subsidence. Land Subsidence Machine Learning SHAP Features Optimization Full Text Additional Declarations No competing interests reported. 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. 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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-3880879","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268256695,"identity":"5fdc9fa5-0a11-439b-aaf1-7d6a8bdde654","order_by":0,"name":"Heng Su","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Su","suffix":""},{"id":268256696,"identity":"9103bc83-e484-4eda-b6d4-3c3ede41a4b9","order_by":1,"name":"Tingting Xu","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Xu","suffix":""},{"id":268256697,"identity":"bb869b53-c750-4230-b220-cd65f8296149","order_by":2,"name":"Xiancai Xion","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACZgglB6HYSNBiTIIWKEhsIFqLwXHmYw+/ttml97fnGDB8KDvMwD+7Ab8WyWa2dGOZM8m5M868MWCcce4wg8SdA/i18DPzmElLVBzI3SCRY8DM23aYwUAiAb8WNrAWgwPpBiAtf4nRArJF8kPFgQSwFkZitAD9kibNcCbZcMaZZwUHe86l80jcIKDF4PzhY5I/2+zk+duTNz74UWYtxz+DgBYQYOYBUwkMB4AkD2H1QMD4A6plFIyCUTAKRgFWAAALeTrwaN/cpwAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Institute of Surveying and Monitoring for Planning and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Xiancai","middleName":"","lastName":"Xion","suffix":""},{"id":268256698,"identity":"c6733409-e899-40fa-a7cb-8f525049478c","order_by":3,"name":"Aohua Tian","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Aohua","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-01-20 06:59:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3880879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3880879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50231976,"identity":"97a0fa99-5383-4dc1-bd97-b77c1af76193","added_by":"auto","created_at":"2024-01-26 21:52:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1928654,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3880879/v1_covered_9b680fa9-6f97-45e0-9e3c-b90545642b55.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of land subsidence prediction features based on machine learning and SHAP value with Sentinel-1 InSAR Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Land Subsidence, Machine Learning, SHAP, Features Optimization","lastPublishedDoi":"10.21203/rs.3.rs-3880879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3880879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand subsidence has always been a concern of geoscience, and exploring the factors affecting land subsidence to predict future land subsidence is essential research. 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