Groundwater Potential and Managed Aquifer Recharge Suitability Assessment using GIS-AHP and Machine Learning in the upstream part of Awash River, Ethiopia

Groundwater Potential and Managed Aquifer Recharge Suitability Assessment using GIS-AHP and Machine Learning in the upstream part of Awash River, Ethiopia | 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 Groundwater Potential and Managed Aquifer Recharge Suitability Assessment using GIS-AHP and Machine Learning in the upstream part of Awash River, Ethiopia Muauz Redda, Behailu Birhanu, Bedru Hussien This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8850489/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Managed aquifer recharge (MAR) is a key strategy for enhancing groundwater storage, reducing overexploitation, and improving climate resilience in semiarid regions. However, its effectiveness depends on accurate identification of hydrogeologically suitable recharge zones. This study developed an integrated Geographic Information System–Analytical Hierarchy Process (GIS–AHP) and ensemble machine learning (ML) framework to assess groundwater potential and MAR suitability in the upstream part of the Awash River in Ethiopia. Eleven thematic layers were weighted using AHP to derive a Groundwater Potential Index (GPI). Geophysical parameters from vertical electrical soundings, transmissivity from pumping tests, and borehole yield data were used for the calibration and independent validation. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models were trained using geophysical predictors and GPI, and combined into an ensemble MAR probability model. The AHP-derived GPI exhibited a good predictive performance against the observed borehole yield (receiver operating characteristic area under the curve, ROC–AUC = 0.731; precision–recall area under the curve, PR–AUC = 0.79). The ensemble ML model showed excellent agreement with the observed MAR conditions (ROC, AUC = 0.913; PR, AUC = 0.946). High to very high MAR suitability zones cover approximately 39% of the basin and are predominantly associated with fractured volcanic formations, moderate slopes, and high lineament density, whereas low-suitability areas correspond to steep terrain and urbanized regions. Integrating expert-based multi-criteria decision analysis with data-driven ML substantially improved MAR site identification under complex volcanic hydrogeological conditions. The proposed framework provides a robust and transferable decision support tool for basin-scale MAR planning. Analytical Hierarchy Process Ensemble Modeling Groundwater Potential Index Machine Learning Managed Aquifer Recharge (MAR) ROC–AUC Precision–Recall Curve Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor invited by journal 27 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 20 Feb, 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. 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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-8850489","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603545203,"identity":"e9bf7abe-4b1c-4b1a-a021-de1f2d4c4989","order_by":0,"name":"Muauz Redda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHAD/o8PPgApNnYS9BgbzgBpYSZBi5k0D4gipEV3RvIzyR812+TN2xvSpG1+bZPnY2Zg/PAxB4/JN9LMJCSO3Tacc+bAYevcvtuGbcwMzJIzt+HTkmAmYcB2m3GGRGLj7dye24xALWzMvHi1pH+TSPh3236G/GMGacue2/ZEaMkxkzjYdjtxhgQbkzTDj9uJhLWceVNs2dh3O3kGTw6zYW/D7eQ2ZsZm/H45nr7x5o9vt21nsJ9hfPDjz23b+e3NBz98xKMFFTC2gckGYtWDwB9SFI+CUTAKRsFIAQCfKlHVOoVhygAAAABJRU5ErkJggg==","orcid":"","institution":"Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Muauz","middleName":"","lastName":"Redda","suffix":""},{"id":603545204,"identity":"7944b4d5-3954-4a67-be38-42b5651696ef","order_by":1,"name":"Behailu Birhanu","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Behailu","middleName":"","lastName":"Birhanu","suffix":""},{"id":603545205,"identity":"66ee35af-e94a-4acf-9966-a888a5228144","order_by":2,"name":"Bedru Hussien","email":"","orcid":"","institution":"Addis Ababa Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Bedru","middleName":"","lastName":"Hussien","suffix":""}],"badges":[],"createdAt":"2026-02-11 10:41:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8850489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8850489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104417008,"identity":"ec1204bb-4890-42dd-bdea-cfbe953fa4f1","added_by":"auto","created_at":"2026-03-11 13:17:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1909161,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8850489/v1_covered_65946221-e973-4b4a-b16b-85edee698ad3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Groundwater Potential and Managed Aquifer Recharge Suitability Assessment using GIS-AHP and Machine Learning in the upstream part of Awash River, Ethiopia","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":"Analytical Hierarchy Process, Ensemble Modeling, Groundwater Potential Index, Machine Learning, Managed Aquifer Recharge (MAR), ROC–AUC, Precision–Recall Curve","lastPublishedDoi":"10.21203/rs.3.rs-8850489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8850489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eManaged aquifer recharge (MAR) is a key strategy for enhancing groundwater storage, reducing overexploitation, and improving climate resilience in semiarid regions. However, its effectiveness depends on accurate identification of hydrogeologically suitable recharge zones. This study developed an integrated Geographic Information System\u0026ndash;Analytical Hierarchy Process (GIS\u0026ndash;AHP) and ensemble machine learning (ML) framework to assess groundwater potential and MAR suitability in the upstream part of the Awash River in Ethiopia. Eleven thematic layers were weighted using AHP to derive a Groundwater Potential Index (GPI). Geophysical parameters from vertical electrical soundings, transmissivity from pumping tests, and borehole yield data were used for the calibration and independent validation. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models were trained using geophysical predictors and GPI, and combined into an ensemble MAR probability model. The AHP-derived GPI exhibited a good predictive performance against the observed borehole yield (receiver operating characteristic area under the curve, ROC\u0026ndash;AUC\u0026thinsp;=\u0026thinsp;0.731; precision\u0026ndash;recall area under the curve, PR\u0026ndash;AUC\u0026thinsp;=\u0026thinsp;0.79). The ensemble ML model showed excellent agreement with the observed MAR conditions (ROC, AUC\u0026thinsp;=\u0026thinsp;0.913; PR, AUC\u0026thinsp;=\u0026thinsp;0.946). High to very high MAR suitability zones cover approximately 39% of the basin and are predominantly associated with fractured volcanic formations, moderate slopes, and high lineament density, whereas low-suitability areas correspond to steep terrain and urbanized regions. Integrating expert-based multi-criteria decision analysis with data-driven ML substantially improved MAR site identification under complex volcanic hydrogeological conditions. 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