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

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 mont...

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主要な著者:	Murphy Bonkogia Lomboli, Opeyeolu Timothy Laseinde, Clinton Ohis Aigbavboa
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2026-05-01
シリーズ:	Scientific Reports
主題:	Urban water demand forecasting Rainfall forecasting Machine learning Gaussian process regression Time-series forecasting
オンライン･アクセス:	https://doi.org/10.1038/s41598-026-51831-1
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