Explainable ensemble models for predicting wall thickness loss of water pipes

Water Distribution Networks (WDNs) are susceptible to pipe failures with significant consequences. Predicting wall-thickness loss in pipes is vital for proactive maintenance and asset management. This study develops optimized, explainable machine learning models for this purpose. Data from four WDNs...

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Autors principals:	Ridwan Taiwo, Abdul-Mugis Yussif, Mohamed El Amine Ben Seghier, Tarek Zayed
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2024-04-01
Col·lecció:	Ain Shams Engineering Journal
Matèries:	Water pipelines Wall thickness Machine learning Ensemble learning SHAP
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2090447924000054
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