A Data-Driven Machine Learning Clustering of Rainfall Patterns: Is Reclassification of Tanzanian Rainfall Climate Zones Needed?

A Data-Driven Machine Learning Clustering of Rainfall Patterns: Is Reclassification of Tanzanian Rainfall Climate Zones Needed? | 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 A Data-Driven Machine Learning Clustering of Rainfall Patterns: Is Reclassification of Tanzanian Rainfall Climate Zones Needed? Hussein Abubakar Bakiri, Hadija Ramadhan Mbembati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9021147/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Water Conservation Science and Engineering → Version 1 posted 12 You are reading this latest preprint version Abstract The wider accessibility of rainfall data and data-driven analytical approaches has boosted climate regionalization research worldwide, particularly in Tanzania. However, despite substantial research on rainfall trends and machine learning applications in Tanzania, we didn’t find a research study that has systematically analyzed the possibility for reclassification of the country's traditional rainfall climatic zones using data-driven approaches. As a result, this study fills that gap by investigating long-term rainfall variability and determining if the present climatic zoning is still valid under observed rainfall patterns. The study used a quantitative and comparative research design with 40 years of monthly rainfall data (1980-2020), mostly from the GPCC and CHIRPS satellite datasets. Machine learning techniques were used to reclassify zones: k-means, hierarchical clustering, and Partitioning Around Medoids (PAM) algorithms. The study also performed cluster validation and zone's agreement analysis (data-driven vs traditional zones) using silhouette, chi-square tests, Cramer's V, Cohen's Kappa, and the Adjusted Rand Index (ARI). The results show significant interannual rainfall variability among zones, with no statistically significant long-term trends. Agreement analysis, on the other hand, shows that traditional zoning is robust (it agrees with the data-driven clustering), despite small divergence within some transitional zones. The divergence indicates that some zones have internal heterogeneity and there is only a need for intra-zonal reclassification. The study offers an analytical framework for improving climate zoning in Tanzania, enhances geographic precision in agricultural planning, water resource management, and climate adaption measures. Rainfall Machine Learning Reclassification Climate zones Data-driven classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Water Conservation Science and Engineering → Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 22 Mar, 2026 Submission checks completed at journal 22 Mar, 2026 First submitted to journal 03 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. 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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-9021147","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611012589,"identity":"f29fad77-4078-4722-b6ab-5d672dddf39e","order_by":0,"name":"Hussein Abubakar Bakiri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACAxDB2MCQwMDAfADIlJAhRQtbAkgLDylaeMBswlrMJdIff/i543Ae/4ycz69u1FjwMLAfProBnxbLGTlmkr1nDhdLnDm7zTrnGNBhPGlpN/A67EYOGwNv2+HEhuO924xz2IBaJHjMCGhJf/zxL1DL/MM8z4xz/hGlJcFAGmTLhuM9zI9z24jRcuaNmbTsmfRiwzPHzJhz+yR42Aj65TjQYW93WOfJ3Uh+/DnnW50cP/vhY3i1IAM2CTBJrHIQYP5AiupRMApGwSgYOQAAsEdNfehjvdsAAAAASUVORK5CYII=","orcid":"","institution":"The institute of finance management (IFM)","correspondingAuthor":true,"prefix":"","firstName":"Hussein","middleName":"Abubakar","lastName":"Bakiri","suffix":""},{"id":611012590,"identity":"04ea1189-4528-473c-b3a8-087d644f04c0","order_by":1,"name":"Hadija Ramadhan Mbembati","email":"","orcid":"","institution":"University of Dar es Salaam","correspondingAuthor":false,"prefix":"","firstName":"Hadija","middleName":"Ramadhan","lastName":"Mbembati","suffix":""}],"badges":[],"createdAt":"2026-03-03 14:10:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9021147/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9021147/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41101-026-00517-w","type":"published","date":"2026-04-24T16:00:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107928622,"identity":"59e60f21-c63e-4833-b6a9-63fee6f19bcb","added_by":"auto","created_at":"2026-04-27 16:11:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":694434,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscriptAuthorNamesMatching.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9021147/v1_covered_c59bac99-7831-4905-9622-ba3e7d350de1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Data-Driven Machine Learning Clustering of Rainfall Patterns: Is Reclassification of Tanzanian Rainfall Climate Zones Needed?","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"water-conservation-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wcse","sideBox":"Learn more about [Water Conservation Science and Engineering](http://link.springer.com/journal/41101)","snPcode":"41101","submissionUrl":"https://submission.nature.com/new-submission/41101/3","title":"Water Conservation Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rainfall, Machine Learning, Reclassification, Climate zones, Data-driven classification","lastPublishedDoi":"10.21203/rs.3.rs-9021147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9021147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The wider accessibility of rainfall data and data-driven analytical approaches has boosted climate regionalization research worldwide, particularly in Tanzania. 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