Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learnin...

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Bibliographic Details
Main Authors:	Langousis, A. (Author), Papacharalampous, G. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2022
Subjects:	Adaptive boosting Decision trees Flow of water Forecasting Hydraulics machine learning Machine learning Machine-learning Neural networks probabilistic forecasting Probabilistic forecasting Probability distributions Quantile regression Regression algorithms Regression analysis statistical learning Statistical learning Time series time series forecasting Time series forecasting uncertainty Uncertainty Urban water demands forecasting Urban water distribution system urban water distribution systems Urban waters
Online Access:	View Fulltext in Publisher

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View Fulltext in Publisher

Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

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