Forecasting Rainfall and Water Demand for Urban Water Management: A Case Study of the City of Ekurhuleni, South Africa

Forecasting Rainfall and Water Demand for Urban Water Management: A Case Study of the City of Ekurhuleni, South Africa | 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 Article Forecasting Rainfall and Water Demand for Urban Water Management: A Case Study of the City of Ekurhuleni, South Africa Murphy Bonkogia Lomboli, Opeyeolu Timothy Laseinde, Clinton Ohis Aigbavboa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8163885/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study develops and evaluates a leak-safe, monthly forecasting framework for the City of Ekurhuleni, South Africa, covering rainfall and municipal water demand. Here “leak-safe” means that all predictors are built from information that would have been available at the forecast month, with feature engineering, scaling and model validation performed strictly on the training window only. The results are situated within demographic change, addressing the gap in decision-grade, monthly forecasts that jointly model rainfall and municipal demand. Monthly datasets (2011–2025) were cleaned and engineered using past-only features (fixed lags; trailing 3/6/12-month statistics; harmonic month terms; simple trend). Models were trained using MATLAB with 5-fold cross-validation (PCA capped at 95% variance when applied) and benchmarked against persistence, seasonal-naïve, and monthly climatology on a sealed test window. For rainfall, a bagged-trees ensemble achieved strong generalization (test RMSE ≈ 9.13 mm; R² ≈ 0.96), capturing wet-season peaks (Dec–Feb) and dry-season minima (Jun–Aug). For demand, a Matérn-5/2 Gaussian Process delivered positive out-of-sample skill (test RMSE ≈ 17.05 ML/day; R² ≈ 0.76; MAPE ≈ 1.39%), tracking month-to-month movements with mild amplitude damping. A 36-month recursive rollout indicates stable consumption within a narrow band (~ 995–1025 ML/day) and a seasonal rainfall envelope consistent with historical patterns. Census-based trends, growth in formal residential areas and increased in-dwelling/yard tap access support a rising, more metered base load with localized variability. The synthesis suggests prioritizing reliability, active leakage control, targeted equity upgrades, and routine re-forecasting over large capacity expansion, while using rainfall-conditioned scenarios and uncertainty bands for procurement and risk planning. The contribution is a reproducible, decision-grade pipeline that pairs rigorous baselines with actionable 36 months forecasts for urban water resources management. Urban water demand forecasting Rainfall forecasting Machine learning Gaussian process regression Time-series forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Submission checks completed at journal 24 Feb, 2026 First submitted to journal 23 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-8163885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611674840,"identity":"f204fddf-4210-4b4a-9fb1-8be9114000e7","order_by":0,"name":"Murphy Bonkogia Lomboli","email":"data:image/png;base64,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","orcid":"","institution":"University of Johannesburg","correspondingAuthor":true,"prefix":"","firstName":"Murphy","middleName":"Bonkogia","lastName":"Lomboli","suffix":""},{"id":611674841,"identity":"ffd30f49-40ad-4b2e-a6b9-5fa4ab5f44c9","order_by":1,"name":"Opeyeolu Timothy Laseinde","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"Opeyeolu","middleName":"Timothy","lastName":"Laseinde","suffix":""},{"id":611674842,"identity":"795f0dc0-e5e7-4cfb-83fa-399cc7485d6b","order_by":2,"name":"Clinton Ohis Aigbavboa","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"Clinton","middleName":"Ohis","lastName":"Aigbavboa","suffix":""}],"badges":[],"createdAt":"2025-11-20 11:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8163885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8163885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105567090,"identity":"f2571a4b-e639-4db4-929c-0f8a2259453a","added_by":"auto","created_at":"2026-03-27 12:58:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1513094,"visible":true,"origin":"","legend":"","description":"","filename":"MachineLearningBasedForecastingofRainfallandWaterDemandforUrbanWaterPlanningTheCaseofEkurhuleniSouthAfrica.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8163885/v1_covered_16e60af2-fc70-4135-b5bd-9438e71180f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting Rainfall and Water Demand for Urban Water Management: A Case Study of the City of Ekurhuleni, South Africa","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban water demand forecasting, Rainfall forecasting, Machine learning, Gaussian process regression, Time-series forecasting","lastPublishedDoi":"10.21203/rs.3.rs-8163885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8163885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops and evaluates a leak-safe, monthly forecasting framework for the City of Ekurhuleni, South Africa, covering rainfall and municipal water demand. Here \u0026ldquo;leak-safe\u0026rdquo; means that all predictors are built from information that would have been available at the forecast month, with feature engineering, scaling and model validation performed strictly on the training window only. The results are situated within demographic change, addressing the gap in decision-grade, monthly forecasts that jointly model rainfall and municipal demand. Monthly datasets (2011\u0026ndash;2025) were cleaned and engineered using past-only features (fixed lags; trailing 3/6/12-month statistics; harmonic month terms; simple trend). Models were trained using MATLAB with 5-fold cross-validation (PCA capped at 95% variance when applied) and benchmarked against persistence, seasonal-na\u0026iuml;ve, and monthly climatology on a sealed test window. For rainfall, a bagged-trees ensemble achieved strong generalization (test RMSE\u0026thinsp;\u0026asymp;\u0026thinsp;9.13 mm; R\u0026sup2; \u0026asymp; 0.96), capturing wet-season peaks (Dec\u0026ndash;Feb) and dry-season minima (Jun\u0026ndash;Aug). For demand, a Mat\u0026eacute;rn-5/2 Gaussian Process delivered positive out-of-sample skill (test RMSE\u0026thinsp;\u0026asymp;\u0026thinsp;17.05 ML/day; R\u0026sup2; \u0026asymp; 0.76; MAPE\u0026thinsp;\u0026asymp;\u0026thinsp;1.39%), tracking month-to-month movements with mild amplitude damping. A 36-month recursive rollout indicates stable consumption within a narrow band (~\u0026thinsp;995\u0026ndash;1025 ML/day) and a seasonal rainfall envelope consistent with historical patterns. Census-based trends, growth in formal residential areas and increased in-dwelling/yard tap access support a rising, more metered base load with localized variability. The synthesis suggests prioritizing reliability, active leakage control, targeted equity upgrades, and routine re-forecasting over large capacity expansion, while using rainfall-conditioned scenarios and uncertainty bands for procurement and risk planning. The contribution is a reproducible, decision-grade pipeline that pairs rigorous baselines with actionable 36 months forecasts for urban water resources management.\u003c/p\u003e","manuscriptTitle":"Forecasting Rainfall and Water Demand for Urban Water Management: A Case Study of the City of Ekurhuleni, South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 23:17:37","doi":"10.21203/rs.3.rs-8163885/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T13:16:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T13:13:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T21:20:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T19:36:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199788704493612994674680461357832377584","date":"2026-03-24T15:50:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T13:28:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256562259224833632021094332339187199470","date":"2026-03-23T11:16:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171107815790792695764012037775192708987","date":"2026-03-23T10:59:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-23T10:20:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-24T12:41:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-23T13:04:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53865318-fa3e-4ede-9f07-8383b0295758","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T05:09:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 23:17:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8163885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8163885","identity":"rs-8163885","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}