Machine learning-based forecasting of rainfall and water demand for urban water planning: the case of Ekurhuleni, South Africa.

Murphy Bonkogia Lomboli, Opeyeolu Timothy Laseinde, Clinton Ohis Aigbavboa

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Murphy Bonkogia LomboliDepartment of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa. 201515240@student.uj.ac.za.ORCID http://orcid.org/0000-0002-0656-0020

Opeyeolu Timothy LaseindeDepartment of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, South Africa.ORCID http://orcid.org/0000-0001-7005-8951

Clinton Ohis AigbavboaDepartment of Construction Management and Quality Surveying, University of Johannesburg, Johannesburg, South Africa.ORCID http://orcid.org/0000-0003-2866-3706

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study develops and evaluates a data-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 consider rainfall and municipal demand for planning. 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; R

Indexed as

Gaussian process regressionMachine learningRainfall forecastingTime-series forecastingUrban water demand forecasting

Identifiers

PMID42092128

DOI10.1038/s41598-026-51831-1

PMCPMC13338276

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.