A Machine Learning Framework for Enhancing Short-Term Water Demand Forecasting Using Attention-BiLSTM Networks Integrated with XGBoost Residual Correction

A Machine Learning Framework for Enhancing Short-Term Water Demand Forecasting Using Attention-BiLSTM Networks Integrated with XGBoost Residual Correction

Accurate short-term water demand forecasting assumes a pivotal role in optimizing water supply control strategies, constituting a cornerstone of effective water management. In recent times, the rise of machine learning technologies has ushered in hybrid models that exhibit superior performance in th...

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Bibliographic Details
Main Authors: Shihao Shan, Hongzhen Ni, Genfa Chen, Xichen Lin, Jinyue Li
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Water
Subjects:
short-term
water demand forecasting
MIC
attention mechanism
BiLSTM network
XGBoost
Online Access:https://www.mdpi.com/2073-4441/15/20/3605

Internet

https://www.mdpi.com/2073-4441/15/20/3605

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