Ensemble learning paradigms for flow rate prediction boosting

Ensemble learning paradigms for flow rate prediction boosting | 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 Ensemble learning paradigms for flow rate prediction boosting Laurent Kouao Kouadio, Jianxin Liu, Serge Kouamelan Kouamelan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2520334/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jul, 2023 Read the published version in Water Resources Management → Version 1 posted 5 You are reading this latest preprint version Abstract In developing countries, climate change has considerably affected population welfare by increasing drinking water scarcity. Global organizations and governments have initiated many drinking water supply projects to fight against this issue. Most of these projects are led by geophysical companies in partnership with drilling ventures to locate drillings expected to give the recommended flow rate (FR). Known as cheap methods, electrical resistivity profiling (ERP) and vertical electrical sounding (VES) were the most preferred. Unfortunately, the project objective was not achieved due to numerous unsuccessful drillings, thereby creating a huge loss of investments. To reduce the repercussion of unsuccessful drillings, we introduced the ensemble machine learning (EML) paradigms composed of four base learners. The aim is to predict at least 80% of correct FR in the validation set before any drilling operations. Geo-electrical features were defined from the ERP and VES and combined with the collected boreholes data to compose the binary dataset ( FR ≤ 1 m 3 / hr and FR >1 m 3 / hr ) for unproductive and productive boreholes respectively). Then, the dataset is transformed before feeding to the EMLs. As a result, the benchmark and the pasting EMLs performed 85% of good predictions on the validation set whereas the extreme gradient boosting and the stacking performed 86% and 87% respectively. Finally, the correct prediction of FRs will reduce the losses in investment beneficial for funders and state governments, and geophysical and drilling ventures. ensemble machine learning electrical method groundwater flow borehole. Full Text Supplementary Files highlights.docx Cite Share Download PDF Status: Published Journal Publication published 25 Jul, 2023 Read the published version in Water Resources Management → Version 1 posted Editorial decision: Major revisions 03 Jul, 2023 Reviewers agreed at journal 01 Feb, 2023 Reviewers invited by journal 01 Feb, 2023 Editor assigned by journal 29 Jan, 2023 First submitted to journal 29 Jan, 2023 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. 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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-2520334","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":172499639,"identity":"a52fac9a-76f7-4cde-998b-b31200572f44","order_by":0,"name":"Laurent Kouao Kouadio","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"Kouao","lastName":"Kouadio","suffix":""},{"id":172499640,"identity":"f18a4141-2fff-4aaa-8a67-c5333a83cf11","order_by":1,"name":"Jianxin Liu","email":"","orcid":"","institution":"Central South 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Global organizations and governments have initiated many drinking water supply projects to fight against this issue. Most of these projects are led by geophysical companies in partnership with drilling ventures to locate drillings expected to give the recommended flow rate (FR). Known as cheap methods, electrical resistivity profiling (ERP) and vertical electrical sounding (VES) were the most preferred. Unfortunately, the project objective was not achieved due to numerous unsuccessful drillings, thereby creating a huge loss of investments. To reduce the repercussion of unsuccessful drillings, we introduced the ensemble machine learning (EML) paradigms composed of four base learners. The aim is to predict at least 80% of correct FR in the validation set before any drilling operations. Geo-electrical features were defined from the ERP and VES and combined with the collected boreholes data to compose the binary dataset ( FR ≤ 1\u003cem\u003em\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e/\u003cem\u003ehr\u003c/em\u003e and \u003cem\u003eFR\u003c/em\u003e \u0026gt;1 \u003cem\u003em\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e/\u003cem\u003ehr\u003c/em\u003e) for unproductive and productive boreholes respectively). Then, the dataset is transformed before feeding to the EMLs. As a result, the benchmark and the pasting EMLs performed 85% of good predictions on the validation set whereas the extreme gradient boosting and the stacking performed 86% and 87% respectively. 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