Geophysical prospecting and machine learning for estimating landfill leakage risk

Geophysical prospecting and machine learning for estimating landfill leakage risk | 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 Geophysical prospecting and machine learning for estimating landfill leakage risk Hongwei Song, Fan Xia, Hongchao Li, Chao Yang, Jianye Gui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6398682/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 The main methods for monitoring the damage of the anti-seepage layer of leachate are to collect water and soil samples from the landfill site, to determine the content of pollutants through indoor chemical analysis, and ground geophysical prospecting.On the basis of these traditional environmental geological exploration methods, this paper uses GIS (geographic information system) technology combined with machine learning method and impact factor prediction method, the leakage of landfill leachate is effectively predicted. Firstly, the landfill area is meshed with a resolution of 5m×5m, and the corresponding position coordinate information is assigned to each meshing unit, BP neural network model was established by using machine learning method to predict the leakage point of landfill site.Based on an investigation of a landfill site in northern China, 880 test samples and 220 verification samples were selected. The groundwater depth, resistivity, polarizability, whether or not the cophase axis of the radar reflection waveform is abnormal, the relationship between the cophase axis and the position of the nearby monitoring hole, and the half-life time information are selected as the input factors. The output is the leakage probability of each grid cell. The prediction results of two machine learning methods (BP neural network and RBF Neural Network) are compared.The results show that the BP neural network model has better prediction effect, the influence factors of groundwater table depth, resistivity and the relationship between resistivity and location of monitoring hole have the greatest influence on the prediction of seepage probability of impermeable membrane. ANN Resistivity Landfill GIS Leachate 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. 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-6398682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454721547,"identity":"efe69c89-8374-4f69-9e58-d43c56f507da","order_by":0,"name":"Hongwei Song","email":"","orcid":"","institution":"Chinese Academy of Geological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Song","suffix":""},{"id":454721548,"identity":"eaf3fca1-b15a-4307-ac00-2dd0d0bd44a0","order_by":1,"name":"Fan 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