An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas

This study introduces an innovative ensemble data-driven model designed for short-term water demand forecasting within urban areas. By synergistically combining three distinct machine learning approaches—NHiTS, XGBoost regression, and a multi-head 1D convolutional neural network—our model enhances f...

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Bibliographic Details

Published in:	Engineering Proceedings
Main Authors:	Amin E. Bakhshipour, Hossein Namdari, Alireza Koochali, Ulrich Dittmer, Ali Haghighi
Format:	Article
Language:	English
Published:	MDPI AG 2024-09-01
Subjects:	water demand forecasting deep learning ensemble learning time series analysis
Online Access:	https://www.mdpi.com/2673-4591/69/1/69