Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses

Water sustainability in the built environment requires an accurate estimation of residential water end uses (e.g., showers, toilets, faucets, etc.). In this study, we evaluate the performance of four models (Random Forest, RF; Support Vector Machines, SVM; Logistic Regression, Log‐reg; and Neural Ne...

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Veröffentlicht in:	Water resources research Jg. 60; H. 6
Hauptverfasser:	Heydari, Zahra, Stillwell, Ashlynn S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Washington John Wiley & Sons, Inc 01.06.2024 Wiley
Schlagworte:	Algorithms Built environment Choice learning class Classification Comparative analysis Computer models data collection Datasets Decision trees Generative adversarial networks logistic regression Machine learning Mathematical models model validation neural network Neural networks Performance evaluation random forest Regression analysis Regression models residential water smart metering systems Supervised learning support vector machine Support vector machines Synthetic data Toilets Urban environments Water Water metering Water meters Water use
ISSN:	0043-1397, 1944-7973
Online-Zugang:	Volltext
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